| --- |
| 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} |
| } |
| ``` |
