Unsupervised Landslide Detection Based on Pixel-Level Deep Feature Representation Using Bi-Temporal Nanosatellite Imagery

Landslide is a serious geographical disaster, causing human casualties and infrastructure losses. Rapid and accurate detection of the landslide-damaged area is very important for effective disaster response and recovery. For this purpose, various landslide detection approaches using remote sensing images have been studied. Especially, recent deep neural networks have been applied to remote sensing image analysis including landslide detection, which tries to overcome the conventional hand-crafted features. However, these deep learning-based methods usually require lots of labeled datasets which are extremely obtained or made in case of landslide detection. To overcome this problem, this study proposed an unsupervised landslide detection method to learn the pixel-lever deep feature representation. Nanosatellites, which have been launched in large numbers recently due to their low cost and accessibility, are useful for disaster management that requires rapid disaster response and damage analysis. For this purpose, the proposed method in this study was applied to the bi-temporal nanosatellite images, Planetscope. The experimental results showed enhanced detection performance than the conventional approaches using hand-crafted features.