Datasets:
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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 |
Auto-City-Research - Damage Is Not Need
Auto-City-Research
Code:
github.com/Ireliya/auto-city-research
Data:
huggingface.co/datasets/Ireliya/auto-city-research
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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