Dataset Preview
Duplicate
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed because of a cast error
Error code:   DatasetGenerationCastError
Exception:    DatasetGenerationCastError
Message:      An error occurred while generating the dataset

All the data files must have the same columns, but at some point there are 8 new columns ({'primary_damage_value', 'delta_ci_low', 'ci_high', 'tracts', 'model_label', 'delta_vs_primary_damage', 'delta_ci_high', 'ci_low'}) and 1 missing columns ({'replicate'}).

This happened while the csv dataset builder was generating data using

hf://datasets/Ireliya/auto-city-research/data/derived/harvey_external_validation_v1/harvey_external_validation_metrics.csv (at revision 17f2fd96df09e2c37656aea691280f9dec9d3f6a), ['hf://datasets/Ireliya/auto-city-research@17f2fd96df09e2c37656aea691280f9dec9d3f6a/data/derived/harvey_external_validation_v1/harvey_external_validation_bootstrap.csv', 'hf://datasets/Ireliya/auto-city-research@17f2fd96df09e2c37656aea691280f9dec9d3f6a/data/derived/harvey_external_validation_v1/harvey_external_validation_metrics.csv', 'hf://datasets/Ireliya/auto-city-research@17f2fd96df09e2c37656aea691280f9dec9d3f6a/data/derived/harvey_external_validation_v1/harvey_nfip_tract_outcomes.csv']

Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1837, in _prepare_split_single
                  writer.write_table(table)
                  ~~~~~~~~~~~~~~~~~~^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, in write_table
                  self._write_table(pa_table, writer_batch_size=writer_batch_size)
                  ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              aggregation: string
              outcome: string
              model: string
              model_label: string
              metric: string
              value: double
              tracts: int64
              ci_low: double
              ci_high: double
              primary_damage_value: double
              delta_vs_primary_damage: double
              delta_ci_low: double
              delta_ci_high: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1847
              to
              {'replicate': Value('int64'), 'aggregation': Value('string'), 'outcome': Value('string'), 'model': Value('string'), 'metric': Value('string'), 'value': Value('float64')}
              because column names don't match
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
                  parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
                                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~^
                      builder, max_dataset_size_bytes=max_dataset_size_bytes
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
                  builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
                  ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ~~~~~~~~~~~~~~~~~~~~~~~~~~^
                      gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
                      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                  ):
                  ^
                File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1839, in _prepare_split_single
                  raise DatasetGenerationCastError.from_cast_error(
                  ...<4 lines>...
                  )
              datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
              
              All the data files must have the same columns, but at some point there are 8 new columns ({'primary_damage_value', 'delta_ci_low', 'ci_high', 'tracts', 'model_label', 'delta_vs_primary_damage', 'delta_ci_high', 'ci_low'}) and 1 missing columns ({'replicate'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/Ireliya/auto-city-research/data/derived/harvey_external_validation_v1/harvey_external_validation_metrics.csv (at revision 17f2fd96df09e2c37656aea691280f9dec9d3f6a), ['hf://datasets/Ireliya/auto-city-research@17f2fd96df09e2c37656aea691280f9dec9d3f6a/data/derived/harvey_external_validation_v1/harvey_external_validation_bootstrap.csv', 'hf://datasets/Ireliya/auto-city-research@17f2fd96df09e2c37656aea691280f9dec9d3f6a/data/derived/harvey_external_validation_v1/harvey_external_validation_metrics.csv', 'hf://datasets/Ireliya/auto-city-research@17f2fd96df09e2c37656aea691280f9dec9d3f6a/data/derived/harvey_external_validation_v1/harvey_nfip_tract_outcomes.csv']
              
              Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)

Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.

replicate
int64
aggregation
string
outcome
string
model
string
metric
string
value
float64
0
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.592598
0
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.464168
0
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.606615
0
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.666667
1
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.61546
1
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.464041
1
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.516449
1
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.6
2
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.554692
2
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.437898
2
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.636952
2
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.533333
3
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.595004
3
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.4391
3
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.570071
3
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.533333
4
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.497577
4
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.375625
4
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.543373
4
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.533333
5
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.672556
5
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.518738
5
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.582732
5
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.6
6
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.502991
6
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.375086
6
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.436699
6
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.4
7
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.594416
7
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.457829
7
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.634333
7
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.466667
8
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.64684
8
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.511257
8
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.572757
8
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.633333
9
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.574682
9
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.439652
9
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.508727
9
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.5
10
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.540573
10
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.418919
10
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.622462
10
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.466667
11
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.448469
11
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.333849
11
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.53739
11
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.4
12
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.624684
12
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.499811
12
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.5773
12
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.5
13
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.567911
13
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.443404
13
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.572245
13
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.566667
14
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.551528
14
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.426272
14
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.57161
14
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.566667
15
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.51195
15
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.39602
15
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.519127
15
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.533333
16
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.612159
16
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.460739
16
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.539285
16
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.566667
17
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.537037
17
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.400705
17
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.564223
17
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.633333
18
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.536196
18
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.395434
18
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.49822
18
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.533333
19
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.565786
19
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.423311
19
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.491798
19
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.533333
20
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.582432
20
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.445144
20
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.580634
20
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.633333
21
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.578977
21
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.445309
21
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.559407
21
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.633333
22
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.600932
22
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.456591
22
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.586473
22
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.5
23
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.487241
23
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.36708
23
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.447037
23
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.4
24
mean
claim_count
damage_area_weighted_mean_severity
spearman_rho
0.521162
24
mean
claim_count
damage_area_weighted_mean_severity
kendall_tau
0.403369
24
mean
claim_count
damage_area_weighted_mean_severity
ndcg_at_20pct
0.550352
24
mean
claim_count
damage_area_weighted_mean_severity
top20_recall
0.5
End of preview.

Auto-City-Research - Damage Is Not Need

Auto-City-Research logo Auto-City-Research

GitHub Code: github.com/Ireliya/auto-city-research
Hugging Face Data: huggingface.co/datasets/Ireliya/auto-city-research
Project website Project: ireliya.github.io/auto-city-research

This repository is the lightweight, privacy-safe reproducibility dataset for the Urban Cup 2026 Competition 2 project:

Damage Is Not Need: Auditing Post-Disaster Priority Disagreement with Multi-Source Urban Evidence

Research Scope

The study audits where a ranking based only on satellite-observed building damage disagrees with transparent multi-source priority scenarios using population exposure, road accessibility, critical facilities, and urban form. It does not provide a ground-truth unmet-need label or an operational emergency allocation model.

Four xBD/xView2 event footprints are included: Hurricane Harvey, Mexico earthquake, Palu tsunami, and Santa Rosa wildfire.

Headline Verification

  • xBD building records: 99,629
  • Reference 500 m cells: 1,448
  • WorldPop 100 m percentile-consensus disagreements: 73
  • WorldPop 100 m exact Top-20% disagreements: 115
  • Legacy WorldPop 1 km checks: 67 and 109
  • Cells passing all fixed non-temporal gates: 4, all in the Mexico footprint
  • Final candidates with supportive historical OSM persistence: 0
  • Harvey external-proxy coverage: 149 SVI/NFIP tracts, 10,134 aggregated NFIP claims, and 41 FEMA assistance ZIP aggregates

The four candidate keys are fixed in data/derived/final_consensus_v1/final_consensus_candidates.csv. The external proxy results are mixed and are released without relabeling them as validation success.

Main Contents

data/derived/xbd_core_v1/                    Parsed xBD building labels
data/derived/xbd_damage_grid_v1/             Reference 500 m damage grid
data/derived/worldpop_context_v1/             WorldPop 1 km comparison
data/derived/worldpop_context_100m_v1/        WorldPop 100 m primary population join
data/derived/priority_mismatch_v1/            Legacy 1 km priority audit
data/derived/priority_mismatch_100m_v1/       Primary 100 m priority audit
data/derived/evidence_hardening_*/            Four baselines and 20,000 weight draws
data/derived/multiscale_*/                    Independent 250/500/1,000 m reconstructions
data/derived/population_resolution_audit_v1/  100 m versus 1 km diagnostics
data/derived/historical_osm_v1/               Pre-event/current OSM sensitivity
data/derived/harvey_external_validation_v1/   Aggregated NFIP boundary test
data/derived/external_proxies_v1/             CDC SVI and FEMA RI-IHP tests
data/derived/final_consensus_v1/              Fixed-gate candidate table and manifest
data/derived/study_overview_v1/                Event, scale, and overview manifests
reports/figures/                               12 result figures plus the study overview
reports/pdf/                                   English paper and Chinese competition material
records/final_evidence_freeze_20260715-1612.md Canonical result record
MANIFEST.csv                                   Byte counts and SHA-256 hashes

Reproduce From GitHub

git clone https://github.com/Ireliya/auto-city-research.git
cd auto-city-research
conda env create -f environment.yml
conda activate city
python scripts/reproduce_core.py --profile final

The command downloads the dataset revision pinned by the GitHub repository, verifies MANIFEST.csv, recomputes both population-resolution analyses and the fixed consensus, checks the four exact candidate keys, and regenerates Figures 11–12. Raw satellite imagery is not required for this deterministic reviewer route.

Privacy And Redistribution Boundary

Included artifacts are derived grids, aggregated tables, selected derived geospatial layers, figures, reports, source/result manifests, and evidence ledgers.

This repository excludes:

  • raw xBD satellite imagery and the full xBD archive;
  • raw WorldPop GeoTIFFs;
  • bulk OSM extracts, tile caches, and raw ohsome response caches;
  • individual NFIP claims and person/household assistance records;
  • credentials, private paths, and temporary files.

Upstream Terms

Because these artifacts combine multiple sources, the dataset card uses license: other; no blanket license overrides upstream terms.

Source Public terms and treatment here
xBD/xView2 Official challenge page; only derived labels, grids, and aggregates are released, not imagery
WorldPop WorldPop licensing FAQ, CC BY 4.0 attribution retained
OpenStreetMap/ohsome ODbL and attribution; derived OSM files remain identifiable and maps carry attribution
CDC/ATSDR SVI Official SVI documentation; only audit fields and aggregate metrics are used
OpenFEMA NFIP and RI-IHP NFIP Claims v2 and RI-IHP v2; only non-person-level aggregates are released
Census TIGER/Line Official technical source; Census attribution retained

See reports/data_access_license_notes.md and data/manifests/auxiliary_data_sources.csv for the detailed source ledger.

Citation

Auto-City-Research. Damage Is Not Need: Auditing Post-Disaster Priority Disagreement with Multi-Source Urban Evidence. Urban Cup 2026 Competition 2.
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