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cityshiftbench-scale122

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Tabular Regression
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openstreetmap
neurips-2026-ed
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cityshiftbench-scale122 / docs /ARTIFACT_RELEASE.md
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# CityShiftBench NeurIPS ED Review Artifact

This file defines the minimum artifact expected for anonymous NeurIPS Evaluations and Datasets review.

Required Review Contents

  • Paper source: paper_no150_v4/main.tex and paper_no150_v4/sections/*.tex.
  • NeurIPS style files: paper_no150_v4/neurips_2026.sty and paper_no150_v4/neurips_2026_formatting/.
  • Table and figure builders: paper_no150_v4/tools/build_neurips_ed_v8_assets.py and paper_no150_v4/tools/build_neurips_ed_full_review.py.
  • Core data registries: data/tile_manifest_122.csv, data/slices/paper_slice_registry_122.csv, and active integrity records.
  • Main result summaries: Scale-122 baseline summaries, SATCA summaries, paired significance CSVs, ablation CSVs, shortcut-control CSVs, and table LaTeX files under paper_no150_v4/tables/.
  • Documentation: docs/DATA_CARD.md, docs/BENCHMARK_CARD.md, docs/COMPUTE_RESOURCES.md, docs/LICENSES.md, and docs/CITYSHIFTBENCH_CROISSANT_METADATA.json.
  • Croissant metadata: the review artifact should include core Croissant fields, source/provenance fields, and Responsible-AI fields, including whether the benchmark contains synthetic data.

Anonymization

For double-blind review, the public artifact should avoid author names in:

  • repository owner or organization name,
  • README badges or contact blocks,
  • commit metadata in a submitted archive when feasible,
  • absolute local paths in documentation.

If an anonymized GitHub repository is used, the OpenReview submission should link to that repository directly. If the repository is not ready, upload an anonymous ZIP artifact through OpenReview and include the same contents listed above. The manuscript PDF itself should remain anonymous; code and data links belong in the OpenReview metadata fields or anonymous supplement.

Large Files

GitHub should not be used for raw OSM JSON dumps, large Sentinel rasters, Photoshop/PSB editing files, or generated preview PDFs. Release these through a dataset host such as Hugging Face, Dataverse, OpenML, or Kaggle. If any asset exceeds 4 GB, include a small sample that reviewers can inspect.

Rebuild Commands

From the project root:

python paper_no150_v4/tools/build_neurips_ed_v8_assets.py
python paper_no150_v4/tools/build_neurips_ed_full_review.py

For full experiment reruns, use docs/COMPUTE_RESOURCES.md and docs/SERVER_SCALE122_RUNBOOK.md.