Datasets › BD-TypoSAT

BD-TypoSAT (Building Damage Typology Satellite Dataset)

Introduced by Yiming Xiao et al. in DamageCAT: A Deep Learning Transformer Framework for Typology-Based Post-Disaster Building Damage Categorization15 Apr 2025 archive 2025-07-28

On Sunday, August 29, 2021, Hurricane Ida struck parts of Louisiana and Mississippi with wind gusts reaching up to 172 mph, leaving more than a million customers without electricity, including the entire New Orleans area. During the disaster, Maxar captured high spatial resolution satellite imagery (at 0.4 m/pixel) and was subsequently made publicly available. The original images were segmented into 512*512-pixel patches to maintain spatial context while enabling detailed analysis. From this process, we generated a dataset of 2,135 triplets, each containing a pre-disaster image, a post-disaster image, and a manually annotated damage categorical mask.

Benchmarks archive 2025-07-28

No leaderboard in the archive resolves to this dataset.

Papers archive 2025-07-28

No paper in the archive has a leaderboard row on this dataset; the archive counts 1 paper for it but never published that list.

Dataset loaders archive 2025-07-28

No loader listed in the archive.

Tasks archive 2025-07-28

License archive 2025-07-28

Apache 2.0

Modalities archive 2025-07-28

Languages archive 2025-07-28

Variants archive 2025-07-28

  • BD-TypoSAT

1 variant name, as the archive lists them.

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