README.md

---
license: etalab-2.0
task_categories:
- image-segmentation
language:
- en
tags:
- remote-sensing
- change-detection
- earth-observation
- building
pretty_name: b-FLAIR-test-spot
size_categories:
- 1K<n<10K
viewer: false
---
# b-FLAIR-test-spot: Building Change Detection Evaluation Dataset (SPOT-6/7)

<img src="./thumbnail.png" alt="b-FLAIR-test-spot" width="500">

## Dataset Description

b-FLAIR-test-spot is an evaluation dataset for building change detection, containing 1,730 annotated image pairs with binary building change masks. This dataset is built from b-FLAIR-test by downloading acquisitions at the same dates and locations for each patch from SPOT-6/7 satellite imagery. It is designed for in-domain evaluation of methods trained on b-FLAIR-spot in particular and provides a rigorous benchmark for bi-temporal building change detection from satellite imagery in general.

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

## Dataset Format

- **Number of pairs:** 1,730 image pairs
- **Image format:** 3-band images (Red, Green, Blue), 64×64 pixels
- **Resolution:** 1.6 meters per pixel 
- **Annotation:** Binary building change masks
- **Geographic coverage:** 9 different French administrative departments
- **Change types:** New building constructions or no change (~30% of pairs show no change)

## Key Features

- Images acquired from SPOT-6/7 satellite at the same dates and locations as b-FLAIR-test from ORTHO-SAT imagery [2]
- Expert-annotated pairs verified by independent assessors
- Focuses exclusively on building construction (no building destruction cases included)
- Compatible with models trained on FLAIR [1], b-FLAIR, or b-FLAIR-spot datasets

## References

[1] Garioud et al. (2023). FLAIR: a country-scale land cover semantic segmentation dataset from multi-source optical imagery. In NeurIPS  
[2] IGN - Institut national de l’information géographique et forestière. (2025). [ORTHO-SAT®: Les ortho-images issues de prises de vues satellitaires](https://geoservices.ign.fr/ortho-sat)

## Citation

If you use this dataset, please cite the following publication:
```bibtex
@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}
}
```