The challenge involves a semantic segmentation task focusing on land cover description using multimodal remote sensing earth observation data. Participants will explore heterogeneous data fusion methods in a real-world scenario. Upon registration, access is granted to a dataset containing 70,000+ aerial imagery patches with pixel-based annotations and 50,000 Sentinel-2 satellite acquisitions.
This project was made possible by our compute partners 2CRSi and NVIDIA.
The score of the challenge was the mIoU.
Our solution was the 8th one (out of 30 teams) with a mIoU equal to 0.62610 🎉.
The podium:
🥇 strakajk - 0.64130
🥈 Breizhchess - 0.63550
🥉 qwerty64 - 0.63510
| Aerial input image | Multi-class label | Multi-class pred |
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View more results on the WandB project.
python src/models/train_model.py <hyperparams args>python src/models/predict_model.py -n {model.ckpt}Chen, L. C., Papandreou, G., Schroff, F., & Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587.
Garioud, A., De Wit, A., Poupée, M., Valette, M., Giordano, S., & Wattrelos, B. (2023). FLAIR# 2: textural and temporal information for semantic segmentation from multi-source optical imagery. arXiv preprint arXiv:2305.14467.
Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J. M., & Luo, P. (2021). SegFormer: Simple and efficient design for semantic segmentation with transformers. Advances in Neural Information Processing Systems, 34, 12077-12090.
@misc{RebergaUrgell:2023,
Author = {Louis Reberga and Baptiste Urgell},
Title = {FLAIR #2},
Year = {2023},
Publisher = {GitHub},
Journal = {GitHub repository},
Howpublished = {\url{https://github.com/association-rosia/flair-2}}
}
Project is distributed under MIT License
Louis
REBERGA 
Baptiste
URGELL