Challenge FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery
Participate in obtaining more accurate maps for a more comprehensive description and a better understanding of our environment! Come push the limits of state-of-the-art semantic segmentation approaches on a large and challenging dataset. Get in touch at [email protected]
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Datapaper : https://arxiv.org/pdf/2305.14467.pdf
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Dataset links : https://ignf.github.io/FLAIR/#FLAIR2 [🛑 for now upon registration to the FLAIR #2 challenge !]
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Challenge page : https://codalab.lisn.upsaclay.fr/competitions/13447
The FLAIR #2 dataset is sampled countrywide and is composed of over 20 billion annotated pixels of very high resolution aerial imagery at 0.2 m spatial resolution, acquired over three years and different months (spatio-temporal domains). Aerial imagery patches consist of 5 channels (RVB-Near Infrared-Elevation) and have corresponding annotation (with 19 semantic classes or 13 for the baselines). Furthermore, to integrate broader spatial context and temporal information, high resolution Sentinel-2 1-year time series with 10 spectral band are also provided. More than 50,000 Sentinel-2 acquisitions with 10 m spatial resolution are available.
Spatial definitions of the FLAIR #2 dataset.
Example of input data (first three columns are from aerial imagery, fourth from Sentinel-2) and corresponding supervision masks (last column).
A two-branch architecture integrating a U-Net with a pre-trained ResNet34 encoder and a U-TAE encompassing a temporal self-attention encoder is presented. The U-TAE branch aims at learning spatio-temporal embeddings from the high resolution satellite time series that are further integrated into the U-Net branch exploiting the aerial imagery. The proposed U-T&T model features a fusion module to extend and reshape the U-TAE embeddings in order to add them towards the U-Net branch.
Overview of the proposed two-branch architecture.
The flair-2-config.yml
file controls paths, hyperparameters and computing ressources. The file requirement.txt
is listing used libraries for the baselines.
To launch a training/inference/metrics computation, you can either use :
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main.py --config_file=flair-2-config.yml
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use the
./notebook/flair-2-notebook.ipynb
notebook guiding you through data visualization, training and testing steps.
A toy dataset (reduced size) is available to check that your installation and the information in the configuration file are correct.
Please note that for participants to the FLAIR #2 challenge on CodaLab, a certain number of constraints must be satisfied (in particular, inference time). All infos are available on the Overview page of the competion.
Model | Input | mIoU |
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baseline U-Net (ResNet34) | aerial imagery | 0.5470 |
baseline U-Net (ResNet34) + metadata + augmentation | aerial imagery | 0.5593 |
baseline U-T&T | aerial and satellite imagery | 0.5594 |
baseline U-T&T + filter clouds + monthly averages + data augmentation | aerial and satellite imagery | 0.5758 |
If you want to submit a new entry, you can open a new issue. Results of the challenge will be reported soon!
The baseline U-T&T + filter clouds + monthly averages + data_augmentation obtains the following confusion matrix:
Please include a citation to the following article if you use the FLAIR #2 dataset:
@article{ign2023flair2,
doi = {10.13140/RG.2.2.30938.93128/2},
url = {https://arxiv.org/pdf/2305.14467.pdf},
author = {Garioud, Anatol and {DE Wit}, Apolline and Poupée, Marc and Valette, Marion and Giordano, Sébastien and Wattrelos, Boris},
title = {FLAIR #2: textural and temporal information for semantic segmentation from multi-source optical imagery},
publisher = {arXiv},
year = {2023}
}
This work was performed using HPC/AI resources from GENCI-IDRIS (Grant 2022-A0131013803). This work was supported by the project "Copernicus / FPCUP” of the European Union, by the French Space Agency (CNES) and by Connect by CNES.
The "OPEN LICENCE 2.0/LICENCE OUVERTE" is a license created by the French government specifically for the purpose of facilitating the dissemination of open data by public administration. If you are looking for an English version of this license, you can find it on the official GitHub page at the official github page.
As stated by the license :
This licence is governed by French law.
This licence has been designed to be compatible with any free licence that at least requires an acknowledgement of authorship, and specifically with the previous version of this licence as well as with the following licences: United Kingdom’s “Open Government Licence” (OGL), Creative Commons’ “Creative Commons Attribution” (CC-BY) and Open Knowledge Foundation’s “Open Data Commons Attribution” (ODC-BY).