Automated landslide detection in SAR wrapped interferograms using a geomorphology-constrained YOLO CNN

Abstract. Slow-moving landslides pose significant hazards in mountain environments, requiring improved detection and monitoring capabilities. Traditional mapping is accurate but time-consuming, while multitemporal InSAR approaches are limited by data complexity and velocity constraints. Wrapped dual-pass DInSAR interferograms offer an alternative by preserving deformation signals without phase unwrapping, enabling detection across a wide range of movement rates.

We present a deep learning framework for the automated detection and classification of active slow landslides in Sentinel-1 wrapped SAR interferograms. The model uses a YOLO convolutional neural network ingesting wrapped phase, an InSAR reliability index, and a terrain morphometric attribute layer. We trained, validated, and tested the network on 2243 labelled DInSAR wrapped phase signals from expert geomorphological interpretation over a 1200 km² sector of the Northern Apennines (Italy), using interferograms from ascending and descending orbits generated with multiple temporal baselines between 6 and 30 days.

The network outputs bounding boxes with movement classification, achieving a mean Average Precision of 0.88 and an F1 score of 0.75. It successfully identifies deformation signals across multiple spatial scales, also in interferograms with low signal-to-noise ratio. Our results demonstrate the potential of wrapped DInSAR data combined with deep learning for efficient regional-scale landslide detection and inventory updating.