b-IAILD: bi-temporal extension of IAILD

Dataset Description

b-IAILD is a temporal extension of the IAILD (Inria Aerial Image Labeling Dataset) dataset [1] focused on building change detection in urban areas. The dataset provides bi-temporal orthoimage pairs with binary building footprint annotations, covering locations in the USA and Austria.

Project page: https://xavibou.github.io/CDviaWTS/

Dataset Summary

• Task: Building change detection via weak temporal supervision
• Coverage: Urban areas in USA (Austin, Chicago, Kitsap) and Austria (Tyrol, Vienna)
• Resolution: 0.6 m/px (standardized)
• Patch Size: 256×256 pixels
• Total Training Pairs: 15,500 (3,100 per city)
• Total Validation Pairs: 2,500 (500 per city)

Dataset Structure

The dataset consists of bi-temporal image pairs with the following temporal coverage:

Location	Original IAILD (t1)	New Acquisition (t2)	Time Gap
Austin, TX	Before 2017	2022	>5 years
Chicago, IL	Before 2017	2023	>6 years
Kitsap, WA	Before 2017	2023	>6 years
Tyrol, Austria	Before 2017	2023	>6 years
Vienna, Austria	Before 2017	2024	>7 years

Dataset Creation

Source Data

The dataset extends the original IAILD training set by adding new orthoimage acquisitions over the same geographic locations:

• USA data: https://earthexplorer.usgs.gov
• Austrian data:
  • Tyrol: https://data-tiris.opendata.arcgis.com
  • Vienna: https://www.wien.gv.at/ma41datenviewer/public/start.aspx

Preprocessing

All images were standardized to a common spatial resolution:

1. Original resolutions (new acquisitions):

   • Vienna: 0.15 m/px
   • Tyrol: 0.20 m/px
   • Austin, Chicago, Kitsap: 0.60 m/px
   • Original IAILD: 0.30 m/px

2. Standardized resolution: 0.60 m/px (all images resampled)

3. Patching: 2500×2500 images split into 256×256 patches with 6-pixel overlap

Annotations

Single-temporal binary building footprint masks are provided for each pair, enabling change detection via weak temporal supervision.

References

[1] E. Maggiori et al. (2017). Can semantic labeling methods generalize to any city? The Inria Aerial Image Labeling Benchmark. In IGARSS

Citation

If you use this dataset, please cite the following publication:

@article{bou2026remote,
  title={Remote Sensing Change Detection via Weak Temporal Supervision},
  author={Bou, Xavier and Vincent, Elliot and Facciolo, Gabriele and Grompone von Gioi, Rafael and Morel, Jean-Michel and Ehret, Thibaud},
  journal={arXiv preprint arXiv:2601.02126},
  year={2026}
}