Adaptive Deep Learning Framework for Rapid Landslide Mapping Using HR-GLDD

The Himalayan terrain has encountered multiple vandalized events that have hampered humans and property. While significant progress has been made in leveraging Earth Observation data for landslide mapping, several critical challenges remain in creating models that can be operational globally. The first limitation is that no high-resolution, globally distributed, and event-diverse dataset is available for landslide segmentation. Inadequacy in data impairs the ability of machine learning models to achieve accurate and robust detection over different terrains since insufficient representation of both landslide and non-landslide classes leads to suboptimal generalization. We provide the High-Resolution Global Landslide Detector Database (HR-GLDD) to fill this critical gap. The unprecedented dataset, derived from PlanetScope imagery with an extraordinary 3-meter pixel resolution, includes a detailed set of landslide instances, including those from the Kalimpong Himalayas in Northeast India, providing never-before-attempted granularity and diversity for global landslide modeling.

The HR-GLDD contains ten independent landslide events, five rainfall-triggered and five seismic, under diverse geomorphological and topographical conditions. Standardized image patches from high-resolution PlanetScope optical satellite imagery in four-spectral-band (red, green, blue, near-infrared) combinations of bands and binary masks delineating landslides are provided. One of the first datasets prepared for landslide research using high-resolution images in artificial intelligence for landslide detection and identification studies is particularly relevant using HR-GLDD.

 

Five state-of-the-art deep learning models were utilized to validate its usefulness by showing stable performance at Kalimpong, verifying the dataset's robustness and transferability. HR-GLDD is publicly available and valuable for calibrating and building models to produce reliable landslide inventories after an event. The constant updating of data from recent landslide events significantly increases its usefulness in developing landslide research and risk assessment.