---
license: etalab-2.0
task_categories:
- image-segmentation
language:
- en
tags:
- remote-sensing
- earth-observation
- change-detection
- building
pretty_name: b-FLAIR-test
size_categories:
- 1K<n<10K
viewer: false
---

# b-FLAIR-test: Building Change Detection Evaluation Dataset

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

## Dataset Description

b-FLAIR-test is an evaluation dataset for building change detection, containing 1,730 annotated image pairs with binary building change masks. This dataset is designed for in-domain evaluation of methods trained on b-FLAIR in particular and provides a rigorous benchmark for bi-temporal building change detection in general.

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

## Dataset Format

- **Number of pairs:** 1,730 image pairs
- **Image format:** 5-band images (Red, Green, Blue, Infrared, Elevation), 512×512 pixels
- **Resolution:** 0.2 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 processed and formatted following the FLAIR dataset [1] procedure from BD ORTHO 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] or b-FLAIR 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). [BD ORTHO®: L’image géographique du territoire national, la France vue du ciel.](https://geoservices.ign.fr/bdortho)

## 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}
}
```