Dataset of DInSAR wrapped phase signals for AI-based automated detection and classification of mass movements

Summary

Abstract With the growing use of Artificial Intelligence (AI) in remote sensing of mass movements, open and suitable datasets for model training and validation are increasingly needed. Although Differential Synthetic Aperture Radar Interferometry (DInSAR) is a widely used technique for studying mass movements, wrapped interferograms remain less exploited, and the importance of geomorphological expertise in their interpretation is not usually emphasised. In this work, we introduce a dataset of DInSAR wrapped phase signals designed to support the development of Deep Learning (DL) models for the automated detection and classification of active slow-moving mass movements. The dataset covers two selected wide areas in the Central European Alps and the Northern Apennines. It contains 4910 expert-annotated DInSAR wrapped phase signals derived from 92 Sentinel-1 interferograms with temporal baselines ranging from 6 days to 1 year, classified into nine distinct landslide and periglacial landform classes after careful geomorphological interpretation. This dataset is expected to support the scientific community in AI-based applications for mass movement research, while also serving as a benchmark for the generation of comparable datasets.