SITS-ExtremeEvents

Official Implementation (code and models) of our WACVw paper: "Leveraging Satellite Image Time Series for Accurate Extreme Event Detection"

Abstract

Climate change is leading to an increase in extreme weather events causing significant environmental damage and loss of life. Early detection of such events is essential for improving disaster response. In this work we propose SITS-Extreme a novel framework that leverages satellite image time series to detect extreme events by incorporating multiple pre-disaster observations. This approach effectively filters out irrelevant changes while isolating disaster-relevant signals enabling more accurate detection. Extensive experiments on both real-world and synthetic datasets validate the effectiveness of SITS-Extreme demonstrating substantial improvements over widely used strong bi-temporal baselines. Additionally we examine the impact of incorporating more timesteps analyze the contribution of key components in our framework and evaluate its performance across different disaster types offering valuable insights into its scalability and applicability for large-scale disaster monitoring.

TODOs

• add environment setup
• add Google Drive links for downloading the processed data
• add example commands for training and testing
• add model checkpoints for reproducing the results
• add reference bibtex
• Test the code from scratch and fix any bugs

0. Environment Setup

1. Data Preparation

Important Note on Data Preparation and Reproducility:

We recommend downloading the processed data directly from the provided links in Section 1.1 for the following reasons:

1. Time efficiency: Preparing the data from scratch can take a significant amount of time (e.g., processing RaVAEn data takes over 7 hours).
2. Consistency and Reproducibility: We inadvertently forgot to set a fixed random seed for the data splitting process. As a result, the exact train/validation/test splits may vary slightly if you choose to prepare the data from scratch using the provided scripts.

For full transparency, we also provide the scripts to prepare the data from scratch in Section 1.2. However, to ensure consistency and reproducibility of the experiments and results as presented in the paper, we strongly suggest using the processed data files, which include the exact splits, available for download in Section 1.1.

1.1. Downloading the data

To run the experiments and reproduce the results, you need to download the processed data from the following links and place them in the data/processed/ directory:

• EuroSAT (hdf5 format, including train/validation/test splits): Google Drive
• RaVAEn (hdf5 format, including train/validation/test splits): Google Drive

1.2. Preparing the data from scratch (optional)

If you want to prepare the data from scratch, you can follow the instructions here:

1. Download the raw data from the following links and place them in the data/raw/ directory:

   • EuroSAT (RGB): GitHub, Download Link
   • RaVAEn:
     • Manually download from the RaVAEn paper website: Google Drive
     • Or alternatively download the data with gdown:

     #!gdown https://drive.google.com/uc?id=1UCNnxaL9pQSkkZQx0aDEWQL0UBXPXkv0 -O fires.zip
     #!gdown https://drive.google.com/uc?id=1CbNGrpK66Hos_TtOEut510k7CSHvSwkl -O landslides.zip
     #!gdown https://drive.google.com/uc?id=1VP3SYgh3bj6uPa4r_bKP-5zFP3JdGin8 -O hurricanes.zip
     #!gdown https://drive.google.com/uc?id=1scjd4gIB_eiNS-CsOyb7Q8rYWnl9TM-L -O floods.zip

2. Unzip the downloaded files:

   # Make sure you are in the 'SITS-ExtremeEvents/' directory
   python data/scripts/unzip_data.py

3. Run the processing scripts to prepare the data:

   • EuroSAT:

   # Make sure you are in the 'SITS-ExtremeEvents/' directory
   python data/scripts/prepare_eurosat_data.py

   • RaVAEn:

   # Make sure you are in the 'SITS-ExtremeEvents/' directory
   python data/scripts/ravaen_to_hdf5.py
   python data/scripts/generate_ravaen_splits.py
   python data/scripts/prepare_ravaen_data.py

2. Experiments

2.1. Training the models

python main.py --config ${CONFIG_FILE} --subconfig ${SUBCONFIG_FILE} --seed ${SEED}

2.2. Evaluating the models

python test.py --default_config ${CONFIG_FILE} --subconfig ${SUBCONFIG_FILE} --checkpoint_folder ${CHECKPOINT_FOLDER}

2.3 Model Checkpoints to reproduce the results (optional)

Reference

If you want to cite our work, you can do so with the following BibTex:

Contact

If you have any questions or need further assistance, please feel free to reach out to us: [email protected].

Acknowledgements

1. We would like to thank the authors of the RaVAEn paper for providing the RaVAEn dataset and the authors of the EuroSAT dataset for making it publicly available.
2. This work is funded by Digital Futures in the project EO-AI4GlobalChange. All experiments were performed using the supercomputing resource Berzelius provided by the National Supercomputer Centre at Linkoping University and the Knut and Alice Wallenberg Foundation. Heng Fang thanks Erik Englesson, Adam Stewart, Dino Ienco, Zhuo Zheng, Sebastian Gerard, Ling Li, and Sebastian Hafner for their feedback on improving the presentation of this paper.