Synergistic use of SAR satellites with deep learning model interpolation for investigating of active landslides in Cuenca, Ecuador - Mohammad Amin Khalili; Silvio Coda; Domenico Calcaterra; Diego Di Martire (Geomatics, Natural Hazards & Risk)
Among the most intense geological disasters, landslides frequently occur throughout the world. These phenomena have been studied using space geodetic techniques, including Global Navigation Satellite Systems (GNSS) and Multi-Temporal Interferometric Synthetic Aperture Radars (MT-InSAR). Nevertheless, complete mapping and analysis of landslides’ surface deformation in most areas can be complicated due to a large diversity in kinematics, such as periods of quiescence and acceleration in the toe and crown. One of these landslides is the Cuenca landslides in Ecuador, where the geological investigation revealed that the toe of the landslides was located in urban areas, with more noticeable deformation effects. In contrast, its crown was located mainly in a rural and green land area. In this study, we show the potential of a synergistic use of COSMO-SkyMed (CSK) and Sentinel-1A (S1A) synthetic aperture radar (SAR) data for comprehensively monitoring the Cuenca landslides. To this aim, we have used Long-Short Term Memory (LSTM) and Convolutional Neural Networks (CNN) as two different Deep Learning Algorithms (DLAs) to integrate results in the temporal and spatial domain, respectively. A cross-comparison of the results was made with the nine GPS-derived deformations and the visual effects (i.e. crack width and pattern) on the field. This validation against GPS observation reveals that the RMSEs of the final MT-InSAR-derived velocity after applying the synergic double band SAR dataset decrease at more than 73% of nine GPS stations.HighlightsSynergic MT-InSAR approach for studying landslide deformation in diverse kinematic areas.Utilized DLAs (LSTM and CNNs) for effective temporal and spatial interpolation of InSAR results.Findings emphasize the potential of multi-sensor SAR and DLAs for landslide monitoring regarding improving the RMSE at nine stations with an average of 73%.