Automating Landslips Segmentation for Damage Assessment: A Comparison Between Deep Learning and Classical Models

Natural disasters have a significant effect in terms of impacted individuals and casualties. Artificial Intelligence (AI) techniques for automatically segmenting landslides from aerial photos is a relatively new field of research. Segmenting landslips quickly and accurately can significantly aid in assessing the damage caused by natural disasters. This research aims to compare the performance of AI techniques with more classical methods for the automatic segmentation of landslides from aerial images for damage assessment. It is presented a dataset of satellite images containing landslides collected in the Broni (Italy) region and annotated to train and test the segmentation model. Both classical image processing techniques, such as thresholding and edge detection, and AI-based methods, such as U-Net, are applied to the dataset. Overall, this research demonstrates that AI-based methods are a promising tool for automatically segmenting landslides from aerial images and can be a powerful asset in assessing the damage caused by natural disasters. The study also highlights the importance of combining classical and AI-based methods for better performance, especially in challenging and complex scenes.