Classifying ground deformation processes, such as landslides, subsidence, deep-seated gravitational slope deformations (DSGSDs), and mining-induced deformations, is key for large-scale hazard assessment and national land use management. Earth observation provides heterogeneous data over the same geographic region, including Interferometric Synthetic Aperture Radar (InSAR) time series, multispectral imagery, and terrain products. However, effectively integrating such spatiotemporal information from multimodal datasets remains a major challenge. In order to fully utilize the rich information contained in the time series and to exploit the complementary strengths of spatial and temporal data, we propose a dual-branch deep learning approach that integrates InSAR ground deformation time series with geospatial information for classifying slow-moving ground deformation processes. To validate the approach, we construct a ground deformation dataset containing over 26,000 Active Deformation Areas (ADAs), labelled into four deformation types: Landslide, Subsidence, DSGSD, and Mining. Results demonstrate that our model achieves an overall classification accuracy exceeding 90% on both ascending and descending test dataset, though confusion remains between certain classes, such as landslides and DSGSD. Explainable AI (XAI) analysis indicates that spatial and morphological features contribute more significantly to classification performance than temporal deformation patterns, with clearer distinctions for subsidence and mining, but more overlap between landslides and DSGSDs. This work highlights the strength of multi modal data fusion method to classify ground deformation processes, while setting the stage for future research.

This work proposes a supervised classifier of ground motion phenomena using as main input the SAR Differential Interferometry (DInSAR) data contained in the European Ground Motion Service (EGMS) datasets. The classifier operates on the active deformation areas (ADAs) extracted from the EGMS data, which are categorized into three deformation classes, i.e. deep-seated gravitational slope deformation (DSGSD), landslide and subsidence. Digital Elevation Models (DEM) and Land Cover maps are used as ancillary input data, whereas landslide and subsidence inventories are employed as ground truth to form the training dataset. The implemented machine learning (ML) classifier employs the Extreme Gradient Boosting technique on a set of features extracted from the input data. Feature importance is analysed to provide an insight on the physical meaning of the implemented classification, moving towards the explainable artificial intelligence paradigm. The results show good classification performance on the test dataset. The classification reliability is evaluated on the unseen data through class probabilities.

Multi-temporal synthetic aperture radar interferometry is nowadays a well-developed remote sensing technique for monitoring Earth’s surface deformation. The European Ground Motion Service (EGMS) is an important component of ground deformation monitoring in Europe, which offers consistent, accurate, and annually updated information about ground displacements related to both natural and anthropogenic phenomena. The policy of providing EGMS products free of charge enables us to leverage the data and develop tools to identify buildings and urban structures that are vulnerable to damage due to differential movement. The tool described in this paper facilitates the generation of a differential deformation map for individual buildings by computing the spatial gradient of deformation for each building and then classifying them into distinct classes according to their gradient values. To demonstrate the reliability and trustworthiness of identified buildings, an uncertainty analysis was performed. Its output includes the relationship between the ratio of average-to-standard deviation of gradient and the density of points in each building. The proposed approach was tested with EGMS data over the area of Catalunya (Spain) from 2015 to 2021. A total of 308 buildings were identified as vulnerable to damage, with around half of them classified as "Low" gradient intensity. This map can serve as the initial step required for an in-depth analysis and risk assessment, aiming to mitigate risks and maintain the stability and safety of buildings.

This work addresses the automatic extraction of active deformation areas (ADAs) using a pan European ground deformation dataset, provided by the European Ground Motion Service. The ADA extraction routine is based on the ADA Finder tool, which selects the persistent scatterers that are likely to belong to a surface affected by a ground deformation phenomenon. The result consists of two European ADA maps, associated with the Sentinel-1 ascending and descending trajectories. A preliminary validation of the European ADA maps is addressed by analysing the detected ADAs in the territory of Valle d'Aosta region (Italy), that is affected by the presence of landslides and deep-seated gravitational slope deformations, showing a limited number of false positives.

This letter aims at investigating the potential of high-resolution (up to$0.7\times0.5\,\,\text{m}^{2}$) synthetic aperture radar (SAR) images in generating digital elevation models (DEMs) using the radargrammetry technique. In this work, we process two SAR images recorded by the Capella Space X-band satellite-borne radar sensor on two consecutive days, October 2 and 3, 2021, over La Palma (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption. We adopt an iterative point-aggregation algorithm to identify matching pixels between the two images; then, the height estimation is performed using a distance minimization routine over the previously identified pairs of matching points. The resultant radargrammetric DEM is validated against a lidar-based DEM for various land cover (LC) classes, showing a good agreement in the areas less affected by lava flow. An estimation of the lava thickness is performed, yielding profiles of the cone area, which are compared to the photogrammetry estimates obtained from the Pléiades mission data.

Generation of Digital Elevation Models (DEMs) using Synthetic Aperture Radar (SAR) data is a valid flexible alternative to other surveying techniques. In this work we adopt radargrammetry on two SAR images recorded by the Capella Space X-band spaceborne sensor on two consecutive days over La Palma Island (Canary Islands, Spain) during the Cumbre Vieja volcanic eruption, with the aim of investigating the potential of these high-resolution (up to 0.7 by 0.5 m2) SAR images in generating precise DEMs. The adopted method employs a multilook-based spatial filter followed by an iterative region-growing algorithm to identify matching pixels between the two images. The resultant radargrammetric DEM is compared with a Lidar-based DEM, showing a good agreement in the areas less affected by lava flow.

Monitoring the internal structure of the snowpack is crucial for managing snow-related hazards such as snow avalanches and snowmelt floods. Recently, the availability of low cost, low power and low-profile frequency-modulated continuous-wave (FMCW) radars at 24 GHz has grown thanks to its potential use the automotive sector. This paper proposes the use of a compact and low-cost FMCW radar as an instrument for improving the study of the snowpack, delivering Snow Density and Liquid Water Content in a fast way. The radar is intended to be used as a snowpit instrument, creating density and Liquid water content (LWC) snow profiles and trying to overcome the customary density cutters (slower and operator-dependent). The theoretical equations of the principle are presented and a preliminary validation by means of a laboratory test is done using dry snow mimicking material, providing encouraging results.

This work addresses the problem of estimating and filtering the Atmospheric Phase Screen from a stack of image phases, extracted from a sequence of SAR images using the PSInSAR approach. Leveraging the assumption that the atmospheric disturbance is a signal with a high spatial correlation and low temporal correlation, a Nonequispaced FFT is used to estimate the spatial spectrum of the input data, which are distributed spatially on a non-uniform grid, then perform a lowpass filter, followed by a highpass temporal filter. The obtained APS estimate is then removed from the input data. The performance of the proposed method is evaluated over Sentinel-1 SAR data to study mining-induced terrain deformations in the Polkovice area, Poland.