Datasets:

squid-bench-anon
/

SQuID

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	"name": "SQuID",
	"description": "\n\t\n\t\t\n\t\tSQuID: Satellite Quantitative Intelligence Dataset\n\t\n\nA comprehensive benchmark for evaluating quantitative spatial reasoning in Vision-Language Models using satellite imagery.\n\n\t\n\t\t\n\t\tDataset Overview\n\t\n\n\n2000 questions testing spatial reasoning on satellite imagery\n587 unique images across four datasets\n1950 auto-labeled questions from segmentation masks (DeepGlobe, EarthVQA, Solar Panels)\n50 human-annotated questions from NAIP imagery with consensus answers\n1577 questions include… See the full description on the dataset page: https://huggingface.co/datasets/squid-bench-anon/SQuID.",
	"alternateName": [
	"squid-bench-anon/SQuID",
	"SQuID"
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	"creator": {
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	"name": "Anonymous Authors",
	"url": "https://huggingface.co/squid-bench-anon"
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	"keywords": [
	"visual-question-answering",
	"English",
	"cc-by-nc-4.0",
	"1K - 10K",
	"parquet",
	"optimized-parquet",
	"Image",
	"Text",
	"Datasets",
	"Dask",
	"Polars",
	"Croissant",
	"🇺🇸 Region: US",
	"satellite-imagery",
	"spatial-reasoning",
	"benchmark",
	"quantitative-reasoning",
	"VLM",
	"language-understanding"
	],
	"license": "https://choosealicense.com/licenses/cc-by-nc-4.0/",
	"url": "https://huggingface.co/datasets/squid-bench-anon/SQuID",
	"dataCollection": "SQuID is constructed from four publicly available remote-sensing sources: EarthVQA (ICCV 2023, building/land-cover masks), DeepGlobe (CVPR Workshops 2018, land-use classification), a photovoltaic panel segmentation dataset (Earth System Science Data 2021), and NAIP imagery (USGS public domain). Auto-labeled questions (1,950) are derived programmatically from segmentation masks via geometric operations (area, count, distance, proximity) using OpenCV and scipy. Human-annotated questions (50) on NAIP imagery were collected from 10 independent annotators per question via a custom web interface, with median/majority voting for ground truth and Mean Median Absolute Deviation (MAD) for tolerance ranges.",
	"dataBiases": "Geographic bias: source datasets concentrate on US (NAIP, photovoltaic) and selected international urban/rural scenes (DeepGlobe, EarthVQA); SQuID is not representative of all global land-use distributions. Resolution bias: ground sampling distance restricted to 0.3m-1.0m; lower-resolution satellite imagery (e.g., Sentinel-2 at 10m) is out of scope. Class-vocabulary bias: questions are restricted to the closed set of land-use classes annotated in source datasets (e.g., building, forest, water, road, cropland, solar panel); open-vocabulary objects are not represented. Question-type bias: 24 question types reflect typical RS analytics tasks (counting, area, proximity, multi-condition); other geospatial reasoning forms (temporal change, 3D structure, semantic captioning) are out of scope.",
	"personalSensitiveInformation": "None. SQuID consists exclusively of overhead satellite/aerial imagery at 0.3m-1.0m ground sampling distance, which does not contain identifiable faces, license plates, or other personally identifiable information. No human subjects beyond voluntary annotators (whose identities are not stored) are involved.",
	"usageInfo": "SQuID is intended for academic research on quantitative spatial reasoning in vision-language models. Per its CC BY-NC 4.0 license (inherited from upstream LoveDA/EarthVQA terms), commercial use is prohibited.",
	"rai:dataCollection": "SQuID is constructed from four publicly available remote-sensing sources: EarthVQA (ICCV 2023, building/land-cover masks), DeepGlobe (CVPR Workshops 2018, land-use classification), a photovoltaic panel segmentation dataset (Earth System Science Data 2021), and NAIP imagery (USGS public domain). Auto-labeled questions (1,950) are derived programmatically from segmentation masks via geometric operations (area, count, distance, proximity) using OpenCV and scipy. Human-annotated questions (50) on NAIP imagery were collected from 10 independent annotators per question via a custom web interface, with median/majority voting for ground truth and Mean Median Absolute Deviation (MAD) for tolerance ranges.",
	"rai:dataBiases": "Geographic bias: source datasets concentrate on US (NAIP, photovoltaic) and selected international urban/rural scenes (DeepGlobe, EarthVQA); SQuID is not representative of all global land-use distributions. Resolution bias: ground sampling distance restricted to 0.3m-1.0m; lower-resolution satellite imagery (e.g., Sentinel-2 at 10m) is out of scope. Class-vocabulary bias: questions are restricted to the closed set of land-use classes annotated in source datasets (e.g., building, forest, water, road, cropland, solar panel); open-vocabulary objects are not represented. Question-type bias: 24 question types reflect typical RS analytics tasks (counting, area, proximity, multi-condition); other geospatial reasoning forms (temporal change, 3D structure, semantic captioning) are out of scope.",
	"rai:personalSensitiveInformation": "None. SQuID consists exclusively of overhead satellite/aerial imagery at 0.3m-1.0m ground sampling distance, which does not contain identifiable faces, license plates, or other personally identifiable information. No human subjects beyond voluntary annotators (whose identities are not stored) are involved.",
	"rai:dataUseCases": "SQuID is intended for academic research on quantitative spatial reasoning in vision-language models. Per its CC BY-NC 4.0 license (inherited from upstream LoveDA/EarthVQA terms), commercial use is prohibited.",
	"rai:dataCollectionType": "Machine-generated from curated segmentation masks with human annotation for validation and tolerance calibration",
	"rai:machineAnnotationTools": "OpenCV 8-connected component analysis, Euclidean distance transforms, and pixel-count area computation over curated segmentation masks; deterministic question templates with embedded minimum-area thresholds and GSD",
	"rai:annotationsPerItem": "10 human annotations per question on the 50-question NAIP validation subset (500 total); tolerance ranges derived via Median Absolute Deviation and applied benchmark-wide by question type",
	"rai:annotatorDemographics": "Volunteer research staff annotators; internal collection with no crowdsourcing and no compensation; annotator identities not stored",
	"rai:dataLimitations": "Questions restricted to mask-derivable quantities (no 3D/height, no multi-temporal change); GSD limited to 0.3m-1.0m; closed 9-class question vocabulary (urban, forest, agricultural, grass, vegetation, barren, water, solar, building); template-generated phrasing; proximity tolerances calibrated on fewer human items than counts/percentages (lower inter-rater reliability, Krippendorff alpha 0.42)",
	"rai:socialImpact": "Supports environmental monitoring, urban planning, and disaster response research by improving quantitative reliability of geospatial AI; overhead imagery at these resolutions contains no personally identifiable information",
	"rai:dataAnnotationProtocol": "10 independent annotations per question on the 50-question NAIP validation subset (500 annotations total, 11 annotators overall). Annotators completed a tutorial, then marked spatial regions, provided counts, or selected categorical answers. Per-question tolerance ranges were derived via Median Absolute Deviation (MAD) around the annotator consensus and applied benchmark-wide by question type.",
	"rai:dataAnnotationPlatform": "Custom grid-based annotation interface built on Turkle (Johns Hopkins University HLTCOE), with grid-cell region selection and a distance ruler; interface details in the supplementary material.",
	"rai:dataAnnotationAnalysis": "Inter-rater reliability: Krippendorff's alpha 0.79 overall (0.959 for counts, 0.424 for proximity). The released anonymized raw annotations and calibration script reproduce the published MAD, ICC and alpha values.",
	"rai:dataSocialImpact": "Supports environmental monitoring, urban planning, and disaster response research by improving quantitative reliability of geospatial AI; overhead imagery at these resolutions contains no personally identifiable information"
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