Advancing debris flow detection based on deep learning model and high-resolution images

Debris flow inventory is an essential task for scientists and managers to mitigate danger to humans, especially in mountainous areas. However, rapid land use and cover change, as well as technological limitations, make it a challenging task. Monitoring debris-flow efforts, especially in hilly places with limited transportation and technology, may improve management to minimize damage caused by this hazard. This work assesses U-shaped deep learning architectures, focusing on the roles of image size, optimization procedures, and data quality in debris flow trace identification using U-Net and U2-Net. While new debris flows can be detected through machine learning modeling, the U-Net model, combined with the Adam optimizer and an input size of 64x64, has been proven to be efficient, accurate, and stable. Small debris traces that can be used for planning debris thickness maps were easily identified in Worldview-2 and UAV images but not in the medium-resolution remote sensing data. When applied to Bat Xat district, Vietnam, the models identified that the distribution of debris flows is not uniform and depends on natural factors, such as rainfall and human-interpolated factors, including the construction of structures. The study also establishes the need to continually assess and incorporate big data for enhanced debris flow hazard assessment and mitigation. Further developments should focus on the effective use of multi-spectral and large-scale topographic data to strengthen disaster risk identification and provide recommendations for disaster risk reduction.