Mitigating Out-of-Distribution Challenges in Landslide Mapping through a Hyperlocal Machine Learning model

In recent years, deep learning models have been used for automated landslide mapping. However, such models often underperform when encountering out-of-distribution (OOD) data (regions or terrain characteristics that are significantly different from those seen during training). To address this issue, we present an automated application powered by Google Earth Engine that constructs hyperlocal machine learning models tailored to specific areas of interest. By defining a limited spatial extent and providing labels specific to the area, our approach mitigates the risk of encountering OOD data, reducing incorrect predictions. The application supports the export of annotated landslide data in both raster and vector formats, enabling users to validate and refine landslide extent. These new high-quality datasets can be incorporated back into existing deep learning models to improve generalizability. With its speed, accuracy, and user-friendly interface, the proposed app aims to facilitate the development of robust landslide identification models, especially in scenarios where data scarcity or geographic diversity poses significant challenges.