Towards automated identification of mass movements in spaceborne interferograms: Comparing expert mapping and deep learning approaches

Abstract. We compare the performance of domain experts and deep learning algorithms in mapping mass movements in alpine scenarios by relying on Sentinel-1 wrapped phase interferograms. First, statistical assessment suggest that same mass movements are not consistently delineated, with generally low intersection over union (IoU) values (0.21–0.41), reflecting the difficulty of consistently distinguishing between active/inactive and coherent/incoherent phase patterns. Second, we tested deep learning (DL) architectures and strategies trained on > 1000 manually mapped coherent phase patterns to identify the best-performing model. Among the tested DL models, U-Net++ with a ResNet-18 encoder and specific optimisations herein developed, achieved the highest performance. We found an IoU of 0.61 relative to the training labels and, when compared in the ten selected case-studies, DL fell within the range of inter-expert variability (mean IoU of 0.494 ± 0.045, Dice coefficient of 0.661 ± 0.041). Our results show that optimised DL approaches allow detecting mass-movement-related patterns in Sentinel-1 interferograms achieving performances in the same range or higher than domain-experts. DL can provide a substantial reduction in manual mapping efforts, consequently achieving higher levels of standardisation, homogeneity and reliability in the generation of mass movement catalogues based on radar interferograms.