A Study on the Performance Comparison of Sediment Movement Detectors Using Unet-Based Models and the Sliding Partitioning Method

Japan is covered by mountainous and hilly terrain and thus is prone to disasters such as landslides caused by earthquakes and heavy rainfall. Currently, the identification of sediment disaster areas is conducted through visual interpretation of aerial photographs taken after these disasters. To improve the efficiency of this process, research has been advancing in the automatic detection of sediment movement areas using deep learning techniques applied to aerial photographs. In this study, we compare the performance of sediment movement detectors using Unet and Unet++, in order to investigate the impact of changes in the loss function parameters during training on detection performance. Additionally, we evaluate the performance improvement when the sliding partitioning method is used for input images to the detectors. Results show that using sliding partitioning and adjusting the value of the loss function coefficient β can improve detection performance.