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<strong class="journal-contentHeaderColor">Abstract.</strong> 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<span>–</span>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 ><span> </span>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<span> </span>±<span> </span>0.045, Dice coefficient of 0.661<span> </span>±<span> </span>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.