Integrating sentinel-2a imagery, DEM data, and spectral feature analysis for landslide detection via fully convolutional networks

Landslides can cause severe damage to property and human life. Identifying their locations and characteristics is crucial for emergency rescue and disaster risk assessment. However, existing methods need help in accurately detecting landslides because of their diverse characteristics and scales, as well as the differences in spectral features and spatial heterogeneity of remote sensing images. To overcome these challenges, a multiscale feature fusion landslide-detection network (MFLD-Net) is proposed. This network utilizes reflectance difference images from pre- and post-landslide Sentinel-2A images, along with digital elevation model (DEM) data. Moreover, a multichannel differential landslide dataset was constructed through spectral analysis of Sentinel-2A images, which facilitates network training and enables differentiation between landslides and other objects with similar spectral features, such as bare soil and buildings. The proposed MFLD-Net was tested in Shuzheng Valley and Detuo town in Sichuan, China, where earthquakes have occurred. The experimental results revealed that compared with advanced deep learning models, MFLD-Net has promising landslide detection performance. This study provides suggestions for selecting optimal deep learning methods and spectral band combinations for landslide detection and offers a publicly available landslide dataset for further research.