Predicting Landslides With Deep Neural Networks and Transfer Learning in Geospatial Analysis

2. Literature Review

Landslide prediction methods have traditionally relied heavily on traditional geotechnical and statistical approaches. Field surveys, analysis of geological and topographical features, and the development of empirical models based on historical landslide occurrences are all part of these methods (Colesanti & Wasowski, 2006). While these approaches have yielded valuable insights, they frequently encounter difficulties in dealing with the complexity and variability of landslide-prone environments. Researchers have been motivated to investigate more advanced techniques due to limitations in scalability and adaptability to diverse terrains (Chen et al., 2016).

Deep learning, particularly CNNs, has shown remarkable success in a variety of geospatial analysis tasks. CNNs have proven effective in automatically learning hierarchical features from satellite imagery in land cover classification, object detection, and change detection (Highland & Bobrowsky, 2008). Deep learning models' ability to discern complex patterns and representations makes them well-suited to the challenges of landslide prediction (de Oliveira et al., 2021).