Landslide4Sense: Reference Benchmark Data and Deep Learning Models for Landslide Detection

• Published in: IEEE transactions on geoscience and remote sensing 2022, Vol.60, p.1-1
• Main Authors: Ghorbanzadeh, Omid, Xu, Yonghao, Ghamisi, Pedram, Kopp, Michael, Kreil, David
• Format: Article
• Language: English
• Subjects: Annotations; Benchmark testing; Benchmarks; Computer vision; Data models; Datasets; Deep learning; Detection; Digital Elevation Models; Digital imaging; Geographical locations; Image classification; Image segmentation; Landslide detection; Landslides; Machine learning; multispectral imagery; natural hazard; Patches (structures); Phased arrays; Remote sensing; Satellite observation; Satellites; Terrain factors; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2022.3215209

This study introduces Landslide4Sense , a reference benchmark for landslide detection from remote sensing. The repository features 3,799 image patches fusing optical layers from Sentinel-2 sensors with the digital elevation model and slope layer derived from ALOS PALSAR. The added topographical information facilitates an accurate detection of landslide borders, which recent researches have shown to be challenging using optical data alone. The extensive data set supports deep learning (DL) studies in landslide detection and the development and validation of methods for the systematic update of landslide inventories. The benchmark data set has been collected at four different times and geographical locations: Iburi (September 2018), Kodagu (August 2018), Gorkha (April 2015), and Taiwan (August 2009). Each image pixel is labelled as belonging to a landslide or not, incorporating various sources and thorough manual annotation. We then evaluate the landslide detection performance of 11 state-of-the-art DL segmentation models: U-Net, ResU-Net, PSPNet, ContextNet, DeepLab-v2, DeepLab-v3+, FCN-8s, LinkNet, FRRN-A, FRRN-B, and SQNet. All models were trained from scratch on patches from one quarter of each study area and tested on independent patches from the other three quarters. Our experiments demonstrate that ResU-Net outperformed the other models for the landslide detection task. We make the multi-source landslide benchmark data (Landslide4Sense) and the tested DL models publicly available at https://www.iarai.ac.at/landslide4sense, establishing an important resource for remote sensing, computer vision, and machine learning communities in studies of image classification in general and applications to landslide detection in particular.