Globally Distributed Landslide Detection Landslides are a natural phenomenon with devastating consequences, frequent in many parts of the world. Thousands of small and medium-sized ground movements follow earthquakes or heavy rain falls. Landslides have become more damaging in recent years due to climate change, population growth, and unplanned urbanization in unstable mountain areas. Early landslide detection is critical for quick response and management of the consequences. Accurate detection provides information on the landslide exact location and extent, which is necessary for landslide susceptibility modeling and risk assessment. Recent advances in machine learning and computer vision combined with a growing availability of satellite imagery and computational resources have facilitated rapid progress in landslide detection. Landslide4Sense aims to promote research in this direction and challenges participants to detect landslides around the globe using multi-sensor satellite images. The images are collected from diverse geographical regions offering an important resource for remote sensing, computer vision, and machine learning communities. Data Description The Landslide4Sense dataset has three splits, training/validation/test, consisting of 3799, 245, and 800 image patches, respectively. Each image patch is a composite of 14 bands that include: Multispectral data from Sentinel-2: B1, B2, B3, B4, B5, B6, B7, B8, B9, B10, B11, B12. Slope data from ALOS PALSAR: B13. Digital elevation model (DEM) from ALOS PALSAR: B14. All bands in the competition dataset are resized to the resolution of ~10m per pixel. The image patches have the size of 128 x 128 pixels and are labeled pixel-wise. The Landslide4Sense dataset is structured as follows: ├── TrainData/ │ ├── img/ | | ├── image_1.h5 | | ├── ... | | ├── image_3799.h5 │ ├── mask/ | | ├── mask_1.h5 | | ├── ... | | ├── mask_3799.h5 ├── ValidData/ | ├── img/ | | ├── image_1.h5 | | ├── ... | | ├── image_245.h5 ├── TestData/ ├── img/ ├── image_1.h5 ├── ... ├── image_800.h5 Note that the label files (mask files) are only accessible in the training set. Mapping classes used in the competition: Class Number Class Name Class Code in the Label 1 Non-landslide 0 2 Landslide 1 Baseline Code The baseline code is available at our GitHub repository. Citation Please cite the following papers if you use the data or the code: @article{ghorbanzadeh2022landslide4sense, title={Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection}, author={Ghorbanzadeh, Omid and Xu, Yonghao and Ghamisi, Pedram and Kopp, Michael and Kreil, David}, journal={IEEE Transactions on Geoscience and Remote Sensing}, volume={60}, pages={1--17}, year={2022}, publisher={IEEE} } @article{ghorbanzadeh2022outcome, title={The outcome of the 2022 landslide4sense competition: Advanced landslide detection from multisource satellite imagery}, author={Ghorbanzadeh, Omid and Xu, Yonghao and Zhao, Hengwei and Wang, Junjue and Zhong, Yanfei and Zhao, Dong and Zang, Qi and Wang, Shuang and Zhang, Fahong and Shi, Yilei and others}, journal={IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing}, volume={15}, pages={9927--9942}, year={2022}, publisher={IEEE} }This research has been conducted at the Institute of Advanced Research in Artificial Intelligence (IARAI).