query_id stringlengths 18 40 | template stringclasses 8
values | source_dataset stringclasses 1
value | scene_id stringclasses 219
values | split stringclasses 3
values | qa_split stringclasses 1
value | disaster stringclasses 20
values | disaster_type stringclasses 7
values | damage_bucket stringclasses 6
values | question stringclasses 108
values | input_layers unknown | gt_tool_chain listlengths 7 10 | gt_answer unknown | gt_type stringclasses 1
value | required_reasoning listlengths 2 5 | difficulty stringclasses 3
values | pipeline_version stringclasses 1
value | created_at stringclasses 1
value | scene_id_b stringclasses 45
values |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
XBD_Q1_santa-rosa-wildfire_00000244_0001 | XBD-Q1 | xBD | santa-rosa-wildfire_00000244 | train | train | santa-rosa-wildfire | fire | no-low-damage | A post-disaster satellite scene from a fire event (santa-rosa-wildfire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 47,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 47,
"classified_buildings": 47,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_santa-rosa-wildfire_00000244_0002 | XBD-Q2 | xBD | santa-rosa-wildfire_00000244 | train | train | santa-rosa-wildfire | fire | no-low-damage | For a post-disaster fire scene (santa-rosa-wildfire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 22,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_santa-rosa-wildfire_00000244_0003 | XBD-Q3 | xBD | santa-rosa-wildfire_00000244 | train | train | santa-rosa-wildfire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 9,
"severe_count": 0,
"class_histogram": {
"no_damage": 9,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_santa-rosa-wildfire_00000244_0004 | XBD-Q8 | xBD | santa-rosa-wildfire_00000244 | train | train | santa-rosa-wildfire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (santa-rosa-wildfire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "santa-rosa-wildfire",
"disaster_type": "fire",
"capture_date": "2017-10-11T19:19:41.000Z",
"gsd": 1.8769936999999999,
"damage_summary": {
"total_buildings": 47,
"classified_buildings": 47,
"counts": {
"no-damage": 47,
"minor-damage": 0,
"major-damage": 0,
"de... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_portugal-wildfire_00001058_0005 | XBD-Q1 | xBD | portugal-wildfire_00001058 | tier3 | train | portugal-wildfire | fire | no-low-damage | A post-disaster satellite scene from a fire event (portugal-wildfire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in s... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 25,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 25,
"classified_buildings": 25,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_portugal-wildfire_00001058_0006 | XBD-Q2 | xBD | portugal-wildfire_00001058 | tier3 | train | portugal-wildfire | fire | no-low-damage | For a post-disaster fire scene (portugal-wildfire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 1,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_portugal-wildfire_00001058_0007 | XBD-Q3 | xBD | portugal-wildfire_00001058 | tier3 | train | portugal-wildfire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_portugal-wildfire_00001058_0008 | XBD-Q8 | xBD | portugal-wildfire_00001058 | tier3 | train | portugal-wildfire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (portugal-wildfire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "portugal-wildfire",
"disaster_type": "fire",
"capture_date": "2017-06-21T11:52:35.000Z",
"gsd": 2.2328367,
"damage_summary": {
"total_buildings": 25,
"classified_buildings": 25,
"counts": {
"no-damage": 25,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_sunda-tsunami_00000008_0009 | XBD-Q1 | xBD | sunda-tsunami_00000008 | tier3 | train | sunda-tsunami | tsunami | no-low-damage | A post-disaster satellite scene from a tsunami event (sunda-tsunami) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in sq... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 18,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 18,
"classified_buildings": 18,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_sunda-tsunami_00000008_0010 | XBD-Q2 | xBD | sunda-tsunami_00000008 | tier3 | train | sunda-tsunami | tsunami | no-low-damage | For a post-disaster tsunami scene (sunda-tsunami), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "none",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 0,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"total_ar... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_sunda-tsunami_00000008_0011 | XBD-Q3 | xBD | sunda-tsunami_00000008 | tier3 | train | sunda-tsunami | tsunami | no-low-damage | A 4×4 grid has been overlaid on a post-disaster tsunami satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_sunda-tsunami_00000008_0012 | XBD-Q8 | xBD | sunda-tsunami_00000008 | tier3 | train | sunda-tsunami | tsunami | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster tsunami scene (sunda-tsunami). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "sunda-tsunami",
"disaster_type": "tsunami",
"capture_date": "2019-01-02T03:17:07.000Z",
"gsd": 1.7746705,
"damage_summary": {
"total_buildings": 18,
"classified_buildings": 18,
"counts": {
"no-damage": 18,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_portugal-wildfire_00001588_0017 | XBD-Q1 | xBD | portugal-wildfire_00001588 | tier3 | train | portugal-wildfire | fire | no-low-damage | A post-disaster satellite scene from a fire event (portugal-wildfire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in s... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 24,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 1
},
"total_buildings": 25,
"classified_buildings": 24,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_portugal-wildfire_00001588_0018 | XBD-Q2 | xBD | portugal-wildfire_00001588 | tier3 | train | portugal-wildfire | fire | no-low-damage | For a post-disaster fire scene (portugal-wildfire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "none",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 0,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"total_ar... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_portugal-wildfire_00001588_0019 | XBD-Q3 | xBD | portugal-wildfire_00001588 | tier3 | train | portugal-wildfire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_portugal-wildfire_00001588_0020 | XBD-Q8 | xBD | portugal-wildfire_00001588 | tier3 | train | portugal-wildfire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (portugal-wildfire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "portugal-wildfire",
"disaster_type": "fire",
"capture_date": "2017-06-21T11:52:35.000Z",
"gsd": 2.2328367,
"damage_summary": {
"total_buildings": 25,
"classified_buildings": 24,
"counts": {
"no-damage": 24,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_midwest-flooding_00000024_0025 | XBD-Q1 | xBD | midwest-flooding_00000024 | train | train | midwest-flooding | flooding | no-low-damage | A post-disaster satellite scene from a flooding event (midwest-flooding) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area i... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 277,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 27
},
"total_buildings": 304,
"classified_buildings": 277,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_midwest-flooding_00000024_0026 | XBD-Q2 | xBD | midwest-flooding_00000024 | train | train | midwest-flooding | flooding | no-low-damage | For a post-disaster flooding scene (midwest-flooding), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 82,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_midwest-flooding_00000024_0027 | XBD-Q3 | xBD | midwest-flooding_00000024 | train | train | midwest-flooding | flooding | no-low-damage | A 4×4 grid has been overlaid on a post-disaster flooding satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 23,
"severe_count": 0,
"class_histogram": {
"no_damage": 23,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G0... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_midwest-flooding_00000024_0028 | XBD-Q8 | xBD | midwest-flooding_00000024 | train | train | midwest-flooding | flooding | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster flooding scene (midwest-flooding). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "midwest-flooding",
"disaster_type": "flooding",
"capture_date": "2019-05-31T16:54:31.000Z",
"gsd": 1.7392077,
"damage_summary": {
"total_buildings": 304,
"classified_buildings": 277,
"counts": {
"no-damage": 277,
"minor-damage": 0,
"major-damage": 0,
"destroy... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_tuscaloosa-tornado_00000000_0029 | XBD-Q1 | xBD | tuscaloosa-tornado_00000000 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | A post-disaster satellite scene from a wind event (tuscaloosa-tornado) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in ... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 9,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 4
},
"total_buildings": 13,
"classified_buildings": 9,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_footp... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_tuscaloosa-tornado_00000000_0030 | XBD-Q2 | xBD | tuscaloosa-tornado_00000000 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | For a post-disaster wind scene (tuscaloosa-tornado), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 4,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_tuscaloosa-tornado_00000000_0031 | XBD-Q3 | xBD | tuscaloosa-tornado_00000000 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | A 4×4 grid has been overlaid on a post-disaster wind satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 3,
"severe_count": 0,
"class_histogram": {
"no_damage": 3,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_tuscaloosa-tornado_00000000_0032 | XBD-Q8 | xBD | tuscaloosa-tornado_00000000 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster wind scene (tuscaloosa-tornado). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "tuscaloosa-tornado",
"disaster_type": "wind",
"capture_date": "2011-05-19T16:48:01.085Z",
"gsd": 1.8569022417,
"damage_summary": {
"total_buildings": 13,
"classified_buildings": 9,
"counts": {
"no-damage": 9,
"minor-damage": 0,
"major-damage": 0,
"destroyed":... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_hurricane-florence_00000398_0033 | XBD-Q1 | xBD | hurricane-florence_00000398 | train | train | hurricane-florence | flooding | no-low-damage | A post-disaster satellite scene from a flooding event (hurricane-florence) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 28,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 28,
"classified_buildings": 28,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_hurricane-florence_00000398_0034 | XBD-Q2 | xBD | hurricane-florence_00000398 | train | train | hurricane-florence | flooding | no-low-damage | For a post-disaster flooding scene (hurricane-florence), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 6,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_hurricane-florence_00000398_0035 | XBD-Q3 | xBD | hurricane-florence_00000398 | train | train | hurricane-florence | flooding | no-low-damage | A 4×4 grid has been overlaid on a post-disaster flooding satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 1,
"severe_count": 0,
"class_histogram": {
"no_damage": 1,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_hurricane-florence_00000398_0036 | XBD-Q8 | xBD | hurricane-florence_00000398 | train | train | hurricane-florence | flooding | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster flooding scene (hurricane-florence). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "hurricane-florence",
"disaster_type": "flooding",
"capture_date": "2018-09-20T16:04:41.000Z",
"gsd": 2.0916247,
"damage_summary": {
"total_buildings": 28,
"classified_buildings": 28,
"counts": {
"no-damage": 28,
"minor-damage": 0,
"major-damage": 0,
"destroye... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_moore-tornado_00000076_0037 | XBD-Q1 | xBD | moore-tornado_00000076 | tier3 | train | moore-tornado | wind | no-low-damage | A post-disaster satellite scene from a wind event (moore-tornado) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in squar... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 240,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 240,
"classified_buildings": 240,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_moore-tornado_00000076_0038 | XBD-Q2 | xBD | moore-tornado_00000076 | tier3 | train | moore-tornado | wind | no-low-damage | For a post-disaster wind scene (moore-tornado), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 94,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_moore-tornado_00000076_0039 | XBD-Q3 | xBD | moore-tornado_00000076 | tier3 | train | moore-tornado | wind | no-low-damage | A 4×4 grid has been overlaid on a post-disaster wind satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 27,
"severe_count": 0,
"class_histogram": {
"no_damage": 27,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G0... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_moore-tornado_00000076_0040 | XBD-Q8 | xBD | moore-tornado_00000076 | tier3 | train | moore-tornado | wind | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster wind scene (moore-tornado). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "moore-tornado",
"disaster_type": "wind",
"capture_date": "2013-05-22T17:26:30.085Z",
"gsd": 1.7298485041,
"damage_summary": {
"total_buildings": 240,
"classified_buildings": 240,
"counts": {
"no-damage": 240,
"minor-damage": 0,
"major-damage": 0,
"destroyed":... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_mexico-earthquake_00000021_0041 | XBD-Q1 | xBD | mexico-earthquake_00000021 | train | train | mexico-earthquake | earthquake | no-low-damage | A post-disaster satellite scene from a earthquake event (mexico-earthquake) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint are... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 283,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 283,
"classified_buildings": 283,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_mexico-earthquake_00000021_0042 | XBD-Q2 | xBD | mexico-earthquake_00000021 | train | train | mexico-earthquake | earthquake | no-low-damage | For a post-disaster earthquake scene (mexico-earthquake), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 78,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_mexico-earthquake_00000021_0043 | XBD-Q3 | xBD | mexico-earthquake_00000021 | train | train | mexico-earthquake | earthquake | no-low-damage | A 4×4 grid has been overlaid on a post-disaster earthquake satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 27,
"severe_count": 0,
"class_histogram": {
"no_damage": 27,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G0... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_mexico-earthquake_00000021_0044 | XBD-Q8 | xBD | mexico-earthquake_00000021 | train | train | mexico-earthquake | earthquake | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster earthquake scene (mexico-earthquake). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "mexico-earthquake",
"disaster_type": "earthquake",
"capture_date": "2017-09-20T17:46:11.000Z",
"gsd": 2.6503215,
"damage_summary": {
"total_buildings": 283,
"classified_buildings": 283,
"counts": {
"no-damage": 283,
"minor-damage": 0,
"major-damage": 0,
"dest... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_hurricane-harvey_00000016_0045 | XBD-Q1 | xBD | hurricane-harvey_00000016 | train | train | hurricane-harvey | flooding | no-low-damage | A post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area i... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 41,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 41,
"classified_buildings": 41,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_hurricane-harvey_00000016_0046 | XBD-Q2 | xBD | hurricane-harvey_00000016 | train | train | hurricane-harvey | flooding | no-low-damage | For a post-disaster flooding scene (hurricane-harvey), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 12,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_hurricane-harvey_00000016_0047 | XBD-Q3 | xBD | hurricane-harvey_00000016 | train | train | hurricane-harvey | flooding | no-low-damage | A 4×4 grid has been overlaid on a post-disaster flooding satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_hurricane-harvey_00000016_0048 | XBD-Q8 | xBD | hurricane-harvey_00000016 | train | train | hurricane-harvey | flooding | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster flooding scene (hurricane-harvey). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "hurricane-harvey",
"disaster_type": "flooding",
"capture_date": "2017-08-31T17:39:25.085Z",
"gsd": 3.1466618,
"damage_summary": {
"total_buildings": 41,
"classified_buildings": 41,
"counts": {
"no-damage": 41,
"minor-damage": 0,
"major-damage": 0,
"destroyed"... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_hurricane-harvey_00000292_0053 | XBD-Q1 | xBD | hurricane-harvey_00000292 | train | train | hurricane-harvey | flooding | no-low-damage | A post-disaster satellite scene from a flooding event (hurricane-harvey) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area i... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 82,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 82,
"classified_buildings": 82,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_hurricane-harvey_00000292_0054 | XBD-Q2 | xBD | hurricane-harvey_00000292 | train | train | hurricane-harvey | flooding | no-low-damage | For a post-disaster flooding scene (hurricane-harvey), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 9,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_hurricane-harvey_00000292_0055 | XBD-Q3 | xBD | hurricane-harvey_00000292 | train | train | hurricane-harvey | flooding | no-low-damage | A 4×4 grid has been overlaid on a post-disaster flooding satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 1,
"severe_count": 0,
"class_histogram": {
"no_damage": 1,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_hurricane-harvey_00000292_0056 | XBD-Q8 | xBD | hurricane-harvey_00000292 | train | train | hurricane-harvey | flooding | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster flooding scene (hurricane-harvey). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "hurricane-harvey",
"disaster_type": "flooding",
"capture_date": "2017-08-31T17:38:50.685Z",
"gsd": 3.024613,
"damage_summary": {
"total_buildings": 82,
"classified_buildings": 82,
"counts": {
"no-damage": 82,
"minor-damage": 0,
"major-damage": 0,
"destroyed":... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_moore-tornado_00000062_0057 | XBD-Q1 | xBD | moore-tornado_00000062 | tier3 | train | moore-tornado | wind | no-low-damage | A post-disaster satellite scene from a wind event (moore-tornado) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in squar... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 21,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 21,
"classified_buildings": 21,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_moore-tornado_00000062_0058 | XBD-Q2 | xBD | moore-tornado_00000062 | tier3 | train | moore-tornado | wind | no-low-damage | For a post-disaster wind scene (moore-tornado), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 1,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_moore-tornado_00000062_0059 | XBD-Q3 | xBD | moore-tornado_00000062 | tier3 | train | moore-tornado | wind | no-low-damage | A 4×4 grid has been overlaid on a post-disaster wind satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_moore-tornado_00000062_0060 | XBD-Q8 | xBD | moore-tornado_00000062 | tier3 | train | moore-tornado | wind | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster wind scene (moore-tornado). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "moore-tornado",
"disaster_type": "wind",
"capture_date": "2013-05-22T17:26:30.085Z",
"gsd": 1.7298485041,
"damage_summary": {
"total_buildings": 21,
"classified_buildings": 21,
"counts": {
"no-damage": 21,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_hurricane-florence_00000297_0061 | XBD-Q1 | xBD | hurricane-florence_00000297 | train | train | hurricane-florence | flooding | no-low-damage | A post-disaster satellite scene from a flooding event (hurricane-florence) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 15,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 15,
"classified_buildings": 15,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_hurricane-florence_00000297_0062 | XBD-Q2 | xBD | hurricane-florence_00000297 | train | train | hurricane-florence | flooding | no-low-damage | For a post-disaster flooding scene (hurricane-florence), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 8,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_hurricane-florence_00000297_0063 | XBD-Q3 | xBD | hurricane-florence_00000297 | train | train | hurricane-florence | flooding | no-low-damage | A 4×4 grid has been overlaid on a post-disaster flooding satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 1,
"severe_count": 0,
"class_histogram": {
"no_damage": 1,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_hurricane-florence_00000297_0064 | XBD-Q8 | xBD | hurricane-florence_00000297 | train | train | hurricane-florence | flooding | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster flooding scene (hurricane-florence). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "hurricane-florence",
"disaster_type": "flooding",
"capture_date": "2018-09-20T16:04:41.000Z",
"gsd": 2.0916247,
"damage_summary": {
"total_buildings": 15,
"classified_buildings": 15,
"counts": {
"no-damage": 15,
"minor-damage": 0,
"major-damage": 0,
"destroye... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_pinery-bushfire_00000897_0065 | XBD-Q1 | xBD | pinery-bushfire_00000897 | tier3 | train | pinery-bushfire | fire | no-low-damage | A post-disaster satellite scene from a fire event (pinery-bushfire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in squ... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 16,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 4
},
"total_buildings": 20,
"classified_buildings": 16,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_pinery-bushfire_00000897_0066 | XBD-Q2 | xBD | pinery-bushfire_00000897 | tier3 | train | pinery-bushfire | fire | no-low-damage | For a post-disaster fire scene (pinery-bushfire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 19,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_pinery-bushfire_00000897_0067 | XBD-Q3 | xBD | pinery-bushfire_00000897 | tier3 | train | pinery-bushfire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 7,
"severe_count": 0,
"class_histogram": {
"no_damage": 6,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 1
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_pinery-bushfire_00000897_0068 | XBD-Q8 | xBD | pinery-bushfire_00000897 | tier3 | train | pinery-bushfire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (pinery-bushfire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "pinery-bushfire",
"disaster_type": "fire",
"capture_date": "2015-11-29T01:09:22.998Z",
"gsd": 1.6592848301,
"damage_summary": {
"total_buildings": 20,
"classified_buildings": 16,
"counts": {
"no-damage": 16,
"minor-damage": 0,
"major-damage": 0,
"destroyed": ... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_socal-fire_00001341_0069 | XBD-Q1 | xBD | socal-fire_00001341 | train | train | socal-fire | fire | no-low-damage | A post-disaster satellite scene from a fire event (socal-fire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in square m... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 74,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 74,
"classified_buildings": 74,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_socal-fire_00001341_0070 | XBD-Q2 | xBD | socal-fire_00001341 | train | train | socal-fire | fire | no-low-damage | For a post-disaster fire scene (socal-fire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 14,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_socal-fire_00001341_0071 | XBD-Q3 | xBD | socal-fire_00001341 | train | train | socal-fire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 2,
"severe_count": 0,
"class_histogram": {
"no_damage": 2,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_socal-fire_00001341_0072 | XBD-Q8 | xBD | socal-fire_00001341 | train | train | socal-fire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (socal-fire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "socal-fire",
"disaster_type": "fire",
"capture_date": "2018-11-14T18:42:58.000Z",
"gsd": 2.5700748,
"damage_summary": {
"total_buildings": 74,
"classified_buildings": 74,
"counts": {
"no-damage": 74,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_portugal-wildfire_00001399_0073 | XBD-Q1 | xBD | portugal-wildfire_00001399 | tier3 | train | portugal-wildfire | fire | no-low-damage | A post-disaster satellite scene from a fire event (portugal-wildfire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in s... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 100,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 2
},
"total_buildings": 102,
"classified_buildings": 100,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_portugal-wildfire_00001399_0074 | XBD-Q2 | xBD | portugal-wildfire_00001399 | tier3 | train | portugal-wildfire | fire | no-low-damage | For a post-disaster fire scene (portugal-wildfire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 46,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_portugal-wildfire_00001399_0075 | XBD-Q3 | xBD | portugal-wildfire_00001399 | tier3 | train | portugal-wildfire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 15,
"severe_count": 0,
"class_histogram": {
"no_damage": 15,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G0... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_portugal-wildfire_00001399_0076 | XBD-Q8 | xBD | portugal-wildfire_00001399 | tier3 | train | portugal-wildfire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (portugal-wildfire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "portugal-wildfire",
"disaster_type": "fire",
"capture_date": "2017-06-21T11:52:35.000Z",
"gsd": 2.2328367,
"damage_summary": {
"total_buildings": 102,
"classified_buildings": 100,
"counts": {
"no-damage": 100,
"minor-damage": 0,
"major-damage": 0,
"destroyed"... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_moore-tornado_00000037_0077 | XBD-Q1 | xBD | moore-tornado_00000037 | tier3 | train | moore-tornado | wind | no-low-damage | A post-disaster satellite scene from a wind event (moore-tornado) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in squar... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 10,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 10,
"classified_buildings": 10,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_moore-tornado_00000037_0078 | XBD-Q2 | xBD | moore-tornado_00000037 | tier3 | train | moore-tornado | wind | no-low-damage | For a post-disaster wind scene (moore-tornado), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "none",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 0,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"total_ar... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_moore-tornado_00000037_0079 | XBD-Q3 | xBD | moore-tornado_00000037 | tier3 | train | moore-tornado | wind | no-low-damage | A 4×4 grid has been overlaid on a post-disaster wind satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_moore-tornado_00000037_0080 | XBD-Q8 | xBD | moore-tornado_00000037 | tier3 | train | moore-tornado | wind | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster wind scene (moore-tornado). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "moore-tornado",
"disaster_type": "wind",
"capture_date": "2013-05-22T17:26:30.085Z",
"gsd": 1.7298485041,
"damage_summary": {
"total_buildings": 10,
"classified_buildings": 10,
"counts": {
"no-damage": 10,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_tuscaloosa-tornado_00000326_0081 | XBD-Q1 | xBD | tuscaloosa-tornado_00000326 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | A post-disaster satellite scene from a wind event (tuscaloosa-tornado) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in ... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 77,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 1
},
"total_buildings": 78,
"classified_buildings": 77,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_tuscaloosa-tornado_00000326_0082 | XBD-Q2 | xBD | tuscaloosa-tornado_00000326 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | For a post-disaster wind scene (tuscaloosa-tornado), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 21,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_tuscaloosa-tornado_00000326_0083 | XBD-Q3 | xBD | tuscaloosa-tornado_00000326 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | A 4×4 grid has been overlaid on a post-disaster wind satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 2,
"severe_count": 0,
"class_histogram": {
"no_damage": 2,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_tuscaloosa-tornado_00000326_0084 | XBD-Q8 | xBD | tuscaloosa-tornado_00000326 | tier3 | train | tuscaloosa-tornado | wind | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster wind scene (tuscaloosa-tornado). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "tuscaloosa-tornado",
"disaster_type": "wind",
"capture_date": "2011-05-19T16:48:01.085Z",
"gsd": 1.8569022417,
"damage_summary": {
"total_buildings": 78,
"classified_buildings": 77,
"counts": {
"no-damage": 77,
"minor-damage": 0,
"major-damage": 0,
"destroyed... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_midwest-flooding_00000285_0085 | XBD-Q1 | xBD | midwest-flooding_00000285 | train | train | midwest-flooding | flooding | no-low-damage | A post-disaster satellite scene from a flooding event (midwest-flooding) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area i... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 11,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 4
},
"total_buildings": 15,
"classified_buildings": 11,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_midwest-flooding_00000285_0086 | XBD-Q2 | xBD | midwest-flooding_00000285 | train | train | midwest-flooding | flooding | no-low-damage | For a post-disaster flooding scene (midwest-flooding), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 3,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_midwest-flooding_00000285_0087 | XBD-Q3 | xBD | midwest-flooding_00000285 | train | train | midwest-flooding | flooding | no-low-damage | A 4×4 grid has been overlaid on a post-disaster flooding satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 1,
"severe_count": 0,
"class_histogram": {
"no_damage": 1,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_midwest-flooding_00000285_0088 | XBD-Q8 | xBD | midwest-flooding_00000285 | train | train | midwest-flooding | flooding | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster flooding scene (midwest-flooding). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "midwest-flooding",
"disaster_type": "flooding",
"capture_date": "2019-05-30T17:35:04.000Z",
"gsd": 1.2524601,
"damage_summary": {
"total_buildings": 15,
"classified_buildings": 11,
"counts": {
"no-damage": 11,
"minor-damage": 0,
"major-damage": 0,
"destroyed"... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_lower-puna-volcano_00000226_0097 | XBD-Q1 | xBD | lower-puna-volcano_00000226 | tier3 | train | lower-puna-volcano | volcano | no-low-damage | A post-disaster satellite scene from a volcano event (lower-puna-volcano) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area ... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 34,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 2
},
"total_buildings": 36,
"classified_buildings": 34,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_lower-puna-volcano_00000226_0098 | XBD-Q2 | xBD | lower-puna-volcano_00000226 | tier3 | train | lower-puna-volcano | volcano | no-low-damage | For a post-disaster volcano scene (lower-puna-volcano), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 9,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"tot... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_lower-puna-volcano_00000226_0099 | XBD-Q3 | xBD | lower-puna-volcano_00000226 | tier3 | train | lower-puna-volcano | volcano | no-low-damage | A 4×4 grid has been overlaid on a post-disaster volcano satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 4,
"severe_count": 0,
"class_histogram": {
"no_damage": 4,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_lower-puna-volcano_00000226_0100 | XBD-Q8 | xBD | lower-puna-volcano_00000226 | tier3 | train | lower-puna-volcano | volcano | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster volcano scene (lower-puna-volcano). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "lower-puna-volcano",
"disaster_type": "volcano",
"capture_date": "2018-05-23T20:59:21.000Z",
"gsd": 2.2423599,
"damage_summary": {
"total_buildings": 36,
"classified_buildings": 34,
"counts": {
"no-damage": 34,
"minor-damage": 0,
"major-damage": 0,
"destroyed... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_pinery-bushfire_00001282_0101 | XBD-Q1 | xBD | pinery-bushfire_00001282 | tier3 | train | pinery-bushfire | fire | no-low-damage | A post-disaster satellite scene from a fire event (pinery-bushfire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in squ... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 11,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 1
},
"total_buildings": 12,
"classified_buildings": 11,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_pinery-bushfire_00001282_0102 | XBD-Q2 | xBD | pinery-bushfire_00001282 | tier3 | train | pinery-bushfire | fire | no-low-damage | For a post-disaster fire scene (pinery-bushfire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "none",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 0,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"total_ar... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_pinery-bushfire_00001282_0103 | XBD-Q3 | xBD | pinery-bushfire_00001282 | tier3 | train | pinery-bushfire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_pinery-bushfire_00001282_0104 | XBD-Q8 | xBD | pinery-bushfire_00001282 | tier3 | train | pinery-bushfire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (pinery-bushfire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "pinery-bushfire",
"disaster_type": "fire",
"capture_date": "2015-11-27T00:39:41.429Z",
"gsd": 2.0315730572,
"damage_summary": {
"total_buildings": 12,
"classified_buildings": 11,
"counts": {
"no-damage": 11,
"minor-damage": 0,
"major-damage": 0,
"destroyed": ... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_mexico-earthquake_00000191_0109 | XBD-Q1 | xBD | mexico-earthquake_00000191 | train | train | mexico-earthquake | earthquake | no-low-damage | A post-disaster satellite scene from a earthquake event (mexico-earthquake) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint are... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 359,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 359,
"classified_buildings": 359,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_mexico-earthquake_00000191_0110 | XBD-Q2 | xBD | mexico-earthquake_00000191 | train | train | mexico-earthquake | earthquake | no-low-damage | For a post-disaster earthquake scene (mexico-earthquake), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 173,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"t... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_mexico-earthquake_00000191_0111 | XBD-Q3 | xBD | mexico-earthquake_00000191 | train | train | mexico-earthquake | earthquake | no-low-damage | A 4×4 grid has been overlaid on a post-disaster earthquake satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 37,
"severe_count": 0,
"class_histogram": {
"no_damage": 37,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G0... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_mexico-earthquake_00000191_0112 | XBD-Q8 | xBD | mexico-earthquake_00000191 | train | train | mexico-earthquake | earthquake | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster earthquake scene (mexico-earthquake). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "mexico-earthquake",
"disaster_type": "earthquake",
"capture_date": "2017-09-20T17:46:11.000Z",
"gsd": 2.6503215,
"damage_summary": {
"total_buildings": 359,
"classified_buildings": 359,
"counts": {
"no-damage": 359,
"minor-damage": 0,
"major-damage": 0,
"dest... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_guatemala-volcano_00000023_0113 | XBD-Q1 | xBD | guatemala-volcano_00000023 | train | train | guatemala-volcano | volcano | no-low-damage | A post-disaster satellite scene from a volcano event (guatemala-volcano) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area i... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 167,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 167,
"classified_buildings": 167,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_guatemala-volcano_00000023_0114 | XBD-Q2 | xBD | guatemala-volcano_00000023 | train | train | guatemala-volcano | volcano | no-low-damage | For a post-disaster volcano scene (guatemala-volcano), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "none",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 0,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"total_ar... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_guatemala-volcano_00000023_0115 | XBD-Q3 | xBD | guatemala-volcano_00000023 | train | train | guatemala-volcano | volcano | no-low-damage | A 4×4 grid has been overlaid on a post-disaster volcano satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_guatemala-volcano_00000023_0116 | XBD-Q8 | xBD | guatemala-volcano_00000023 | train | train | guatemala-volcano | volcano | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster volcano scene (guatemala-volcano). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "guatemala-volcano",
"disaster_type": "volcano",
"capture_date": "2018-06-22T16:55:40.000Z",
"gsd": 1.4085245,
"damage_summary": {
"total_buildings": 167,
"classified_buildings": 167,
"counts": {
"no-damage": 167,
"minor-damage": 0,
"major-damage": 0,
"destroy... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_portugal-wildfire_00000060_0117 | XBD-Q1 | xBD | portugal-wildfire_00000060 | tier3 | train | portugal-wildfire | fire | no-low-damage | A post-disaster satellite scene from a fire event (portugal-wildfire) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in s... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 13,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 1
},
"total_buildings": 14,
"classified_buildings": 13,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_foo... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_portugal-wildfire_00000060_0118 | XBD-Q2 | xBD | portugal-wildfire_00000060 | tier3 | train | portugal-wildfire | fire | no-low-damage | For a post-disaster fire scene (portugal-wildfire), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "none",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 0,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"total_ar... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_portugal-wildfire_00000060_0119 | XBD-Q3 | xBD | portugal-wildfire_00000060 | tier3 | train | portugal-wildfire | fire | no-low-damage | A 4×4 grid has been overlaid on a post-disaster fire satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 0,
"severe_count": 0,
"class_histogram": {
"no_damage": 0,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "none",
"top_k_cells": [
{
"cell_id": "G00",
... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_portugal-wildfire_00000060_0120 | XBD-Q8 | xBD | portugal-wildfire_00000060 | tier3 | train | portugal-wildfire | fire | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster fire scene (portugal-wildfire). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "portugal-wildfire",
"disaster_type": "fire",
"capture_date": "2017-06-21T11:52:35.000Z",
"gsd": 2.2328367,
"damage_summary": {
"total_buildings": 14,
"classified_buildings": 13,
"counts": {
"no-damage": 13,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q1_moore-tornado_00000098_0125 | XBD-Q1 | xBD | moore-tornado_00000098 | tier3 | train | moore-tornado | wind | no-low-damage | A post-disaster satellite scene from a wind event (moore-tornado) has been captured. Generate a complete damage inventory report including: the count of buildings per damage class, the percentage of damaged and severely damaged buildings over all classified buildings, and the total severe-damage footprint area in squar... | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"ComputeDamagePercentages",
"ExportDamageReport",
"Terminate"
] | {
"damage_distribution": {
"no-damage": 135,
"minor-damage": 0,
"major-damage": 0,
"destroyed": 0,
"unclassified": 0
},
"total_buildings": 135,
"classified_buildings": 135,
"damaged_count": 0,
"severe_count": 0,
"damaged_pct": 0,
"severe_pct": 0,
"severe_footprint_m2": 0,
"total_... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"percentage_calculation"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q2_moore-tornado_00000098_0126 | XBD-Q2 | xBD | moore-tornado_00000098 | tier3 | train | moore-tornado | wind | no-low-damage | For a post-disaster wind scene (moore-tornado), the 1024×1024 image has been divided into four quadrants (Q0–Q3). Which quadrant contains the highest severe-damage footprint area? Report the quadrant ID, the count of severely damaged buildings, and the footprint area in m². | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadQuadrantGrid",
"ComputeQuadrantDamageStats",
"RankQuadrantsBySevereDamageArea",
"Terminate"
] | {
"highest_severe_area_quadrant": "Q0",
"severe_count": 0,
"severe_footprint_m2": 0,
"severe_pct_in_quadrant": 0,
"dominant_damage": "no-damage",
"top_k_quadrants": [
{
"cell_id": "Q0",
"building_count": 40,
"severe_count": 0,
"severe_pct": 0,
"severe_area_m2": 0,
"to... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"area_computation",
"ranking"
] | medium | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q3_moore-tornado_00000098_0127 | XBD-Q3 | xBD | moore-tornado_00000098 | tier3 | train | moore-tornado | wind | no-low-damage | A 4×4 grid has been overlaid on a post-disaster wind satellite scene, creating 16 cells (G00–G33). Each cell is scored by normalised damage severity (0=no damage, 1=fully destroyed). Which cell has the highest severity score? Report the cell ID, score, and its building class histogram. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadGrid4x4",
"ComputeCellSeverityScores",
"RankCellsByNormalizedSeverity",
"Terminate"
] | {
"highest_severity_cell": "G00",
"severity_score": 0,
"building_count": 9,
"severe_count": 0,
"class_histogram": {
"no_damage": 9,
"minor_damage": 0,
"major_damage": 0,
"destroyed": 0,
"unclassified": 0
},
"dominant_damage": "no-damage",
"top_k_cells": [
{
"cell_id": "G00"... | direct_xbd_labels_plus_deterministic_geometry | [
"spatial_localization",
"severity_scoring",
"ranking"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
XBD_Q8_moore-tornado_00000098_0128 | XBD-Q8 | xBD | moore-tornado_00000098 | tier3 | train | moore-tornado | wind | no-low-damage | Create a comprehensive scene-level damage summary for this post-disaster wind scene (moore-tornado). Use the building damage labels, target mask class distribution, severe-damage footprint area, and spatial dispersion analysis. Include damage counts, percentages, spatial assessment, and target mask validation. | {
"pre_image": "pre.png",
"post_image": "post.png",
"post_target_mask": "post_target.png",
"damage_polygons_xy": "building_damage_xy.geojson",
"damage_polygons_geo": "building_damage_lnglat.geojson",
"quadrants": "quadrants.geojson",
"grid_4x4": "grid_4x4.geojson"
} | [
"LoadXBDScene",
"LoadDamagePolygons",
"ValidateGSD",
"LoadPrePostMetadata",
"CountDamageDistribution",
"ComputeSevereDamageAreaFromXYGSD",
"LoadTargetMaskHistogram",
"ValidateMaskVsPolygonCounts",
"ExportSceneChangeSummary",
"Terminate"
] | {
"disaster": "moore-tornado",
"disaster_type": "wind",
"capture_date": "2013-05-22T17:26:30.085Z",
"gsd": 1.7298485041,
"damage_summary": {
"total_buildings": 135,
"classified_buildings": 135,
"counts": {
"no-damage": 135,
"minor-damage": 0,
"major-damage": 0,
"destroyed":... | direct_xbd_labels_plus_deterministic_geometry | [
"counting",
"area_computation",
"mask_validation",
"spatial_localization",
"multi_source_synthesis"
] | hard | v1 | 2026-05-13T20:40:43.830608+00:00 | null |
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