Flood Detection Challenge

This repository contains code for our submission to the ETCI 2021 Competition on Flood Detection (Winning Solution #2).

Accompanying paper: Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning.

by Sayak Paul*, Siddha Ganju*.

(*) equal contribution.

Team

• Siddha Ganju
• Sayak Paul

Executing the code

We executed the scripts and notebooks on a Vertex AI Notebook instance. The instance has four Tesla V100 GPUs and its base configuration is n1-standard-16.

We use Python 3.8 and PyTorch 1.9. Apart from the requirements specified in requirements.txt you'd need to install the following as well to run the scripts and notebooks:

$ pip install git+https://github.com/qubvel/segmentation_models.pytorch
$ pip install git+https://github.com/lucasb-eyer/pydensecrf.git

Our scripts and notebooks make use of mixed-precision training (via torch.cuda.amp) and distributed training (via torch.nn.parallelDistributedDataParallel). With this combination we are able to achieve significant boosts in the overall model training time.

Execution instructions for the scripts (src) and notebooks (notebooks) are provided in their respective directories.

Pre-trained weights

For complete reproducibility, we provide the pre-trained weights here. With these weights and the workflow depicted in the notebooks and scripts one should be able to get an IoU of ~0.76 (as per the competition leaderboard) on the test set provided at the competition.

Results

You can verify the reported results here. Just switch to "Test (Phase 2)" after clicking the link.

FAQ

• Create a new Notebooks instance

Acknowledgements

• We are grateful to the ML-GDE program for providing GCP credits to support our experiments.
• Thanks to Charmi Chokshi, and domain experts Nick Leach and Veda Sunkara for insightful discussions.

Citation

@misc{paul2021flood,
      title={Flood Segmentation on Sentinel-1 SAR Imagery with Semi-Supervised Learning}, 
      author={Sayak Paul and Siddha Ganju},
      year={2021},
      eprint={2107.08369},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}