Landslide Detection

Landslide detection research focuses on accurately identifying and mapping landslides using remote sensing data and advanced computational methods to mitigate their devastating impacts. Current efforts heavily utilize deep learning architectures, such as U-Net, LinkNet, and transformer-based models, often incorporating data fusion techniques that combine optical and radar imagery with elevation data to improve accuracy and interpretability. This work is crucial for improving landslide risk assessment, informing hazard mitigation strategies, and enabling more effective emergency response, particularly in light of increasing landslide frequency due to climate change.