Home

Awesome

FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models

Code for the experiments from the paper FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models.

Samples Qualitative results obtained over two example areas, namely USA (top) and Gambia (bottom). from left to right: RGB image, DAFormer, MIC, and FMARS ground truth. Best viewed zoomed in.

arXiv

[!NOTE]
The Dataset is available as parquet files, to be rasterized, or can be generated with the package igarss-fmars-gen.

Installation

Environment Setup

First, please install cuda version 11.0.3 available at https://developer.nvidia.com/cuda-11-0-3-download-archive. It is required to build mmcv-full later.

For this project, we used python 3.8.5. We recommend setting up a new virtual environment:

pyenv install 3.8.17
pyenv global 3.8.17
python -m venv .venv
source .venv/bin/activate

In that environment, the requirements can be installed with:

pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html
pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
pip install -e .

Please, download the MiT-B5 ImageNet weights provided by SegFormer from their OneDrive and put them in the folder pretrained/.

Metadata

Download necessary metadata using:

wget -q https://github.com/links-ads/igarss-fmars-gen/releases/download/v0.1.0/metadata.zip
unzip metadata.zip
rm metadata.zip

Put the metadata in the folder metadata/.

Dataset generation

The Dataset is available as parquet files, to be rasterized, or can be generated with the package igarss-fmars-gen Both the original Maxar Open Data dataset and the annotations genereted with FMARS are needed. An example of data folder structure can be:

data/
├── maxar-open-data/
|   ├── afghanistan-earthquake22/
|   ├── BayofBengal-Cyclone-Mocha-May-23/
|   └── ...
└── annotations/
    ├── train/
    |   ├── afghanistan-earthquake22/
    |   ├── BayofBengal-Cyclone-Mocha-May-23/
    |   └── ...	
    ├── val/
    |   ├── afghanistan-earthquake22/
    |   ├── BayofBengal-Cyclone-Mocha-May-23/
    |   └── ...	
    └── test/
        ├── afghanistan-earthquake22/
        ├── BayofBengal-Cyclone-Mocha-May-23/
        └── ...

It's necessary for the images and the annotations to be in the correct events subfolders. The paths need to be specified in the configuration files under configs/_base_/datasets/ in the fields img_dirs and ann_dirs.

Experiments

To run the experiments reported in the paper, the following commands are used to train and test the three models (SegFormer, DAFormer, MIC).

python -B -O tools/train.py configs/fmars/segformer.py
python -B -O tools/test.py configs/fmars/segformer.py work_dirs/segformer/iter_30000.pth --eval mIoU --show-dir output_images/segformer

python -B -O tools/train.py configs/fmars/daformer_sampler.py
python -B -O tools/test.py configs/fmars/daformer_sampler.py work_dirs/daformer_sampler/iter_30000.pth --eval mIoU --show-dir output_images/daformer_sampler

python -B -O tools/train.py configs/fmars/mic_sampler.py
python -B -O tools/test.py configs/fmars/mic_sampler.py work_dirs/mic_sampler/iter_30000.pth --eval mIoU --show-dir output_images/mic_sampler

Additionally, in the configs/ folder, alternative experiments are available, including the versions of the models trained ignoring the entropy based sampling.

Acknowledgements

FMARS is based on the following open-source projects. We thank their authors for making the source code publicly available.