UNet and Semantic Segmentation Based Landslide Detection System

In the contemporary world Landslides pose huge risks to communities and infrastructure globally. One of the reasons it occurs is due to slope movement caused by urbanization and climate change in recent years. Landslide Detection Systems are an evolving technology that is very crucial for handling the impacts of these natural disasters. LDS plays a vital role in identifying and monitoring landslide occurrences across diverse terrains. This paper aims to propose one such LDS system that can detect landslides using a benchmark dataset called LandSlide4Sense through specialized deep- learning models. The motive is to determine the most effective methods by training them on large amounts of data that can help enhance accuracy and ensure quick detection and response. Through this research, the paper highlights advancements in DL-based semantic segmentation for satellite imagery analysis.