Deformation processes, both natural (e.g., subsidence, landslides, active tectonics) and induced (e.g., associated with mining, construction. groundwater exploitation), result in significant socioeconomic losses worldwide. Accurate detection and classification of these processes are crucial for effective risk management. In this study, we present a novel approach for the automatic classification of deformation processes using Interferometric Synthetic Aperture Radar (InSAR) data and machine learning techniques. Specifically, we use a decision tree-based classification algorithm to train a model capable of recognizing and distinguishing different types of deformation processes using time series of displacements, grouped into Active Deformation Areas (ADAs). We test this methodology in a large area in SE Spain. Our results demonstrate promising performance, with an Area Under the Curve (AUC) > 0.95, identifying several covariates of morphometric, geological, hydrogeological, and geotechnical nature as key factors. This automatic classification of InSAR data holds significant implications for risk management associated with ground deformation, providing a potentially valuable tool for decision makers in urban planning and land management officials.

InSAR has been widely employed in terrain deformation analysis worldwide. Its significant utility in risk management has led to the development of extensive SAR databases, poised for exploration in land-use planning studies. However, this information still requires specific expertise, hindering its accessibility for non-expert users. In this work, we introduce MOVESAR, an SAR-based database designed for training Machine Learning (ML) classification models capable of providing precise information on the type of deformation process and its cause. MOVESAR is also planned to support the development of deformation time series forecasting models.Each row in MOVESAR is spatiotemporally linked to a deformation time series (DTS) obtained through InSAR processing of SAR images from various satellites (SENTINEL 1, ENVISAT, ERS, COSMO-SkyMed, ALOS and TerraSAR-X), collected from previous studies conducted by the Geological Survey of Spain (IGME) and the Centre Tecnológic Telecomunicacions Catalunya (CTTC). Spatially, our database covers a substantial part of the Spanish territory, represented in 60 deformation polygons (with more than 300,000 measurement points or "MPs"), spanning from 1992 to 2020.Each column in MOVESAR represents a covariate potentially related to the six deformation processes compiled in this initial version of the database: piezometric change-induced deformation, landslide in mining environments, soil landslide, constructive subsidence, subsidence in mining environments, and subsidence in dumps. Covariates include geological, morphometric, hydrological, and geotechnical information, as well as data associated with DTS, land use, land cover, and landslide, subsidence and expansive clays susceptibility/hazard. Dynamic variables, including precipitation and DTS, underwent transformation into static variables by extracting statistical measures such as mean, standard deviation, range, and slope.In this study, we present preliminary results from nine ML models trained using MOVESAR: four single base models (nb, knn, lda, and lr), and five ensemble models (rf, gbc, xgboost, lightgbm, and catboost). We discuss the performance of the models and analyze the importance of covariates. Additionally, we evaluate the impact of applying techniques aimed at reducing noise, bias, and model complexity, such as threshold velocity filtering technique (TVF) for eliminating stables MPs, Recursive Feature Elimination (RFE) for covariate reduction, and Cost Sensitive Learning (CSL) for class balancing.Our future work aims to expand the number of covariates, MPs, and classes using the European Ground Motion Service (EGMS) to enrich MOVESAR, establishing it as a nationally valuable database for forthcoming studies on geohazard management. Additionally, we plan to apply spatiotemporal Deep Learning (DL) models incorporating dynamic variables, providing reliable classifications for decision-making in urban planning and national land-use management.This work has been developed thanks to the pre-doctoral grant for the Training of Research Personnel (PRE2021-100044) funded by MCIN/AEI/10.13039/501100011033 and by "FSE invests in your future" within the framework of the SARAI project "Towards a smart exploitation of land displacement data for the prevention and mitigation of geological-geotechnical risks" PID2020-116540RB-C22 funded by MCIN/AEI/10.13039/501100011033.

A recent article using data from the European Ground Motion Service (EGMS) to assess the vulnerability of the Spanish coastline to ground movements was published in October 2024 (López-Vinielles et al., 2024). The study, funded by the “Plan de Recuperación, Transformación y Resiliencia - Financiado por la Unión Europea - Next Generation EU” programme and conducted within the framework of the RISKCOAST project (Ref. SOE3/P4/E0868), the EGMS RASTOOL project (Grant Agreement No. 101048474), and the SARAI project (PID2020-116540RB-C22), examines the coastline's exposure to ground movements and their potential impacts on roads, buildings, and populations.Utilizing a suite of post-processing tools including ADAfinder, 9,010 Active Deformation Areas (ADAs) across 805 coastal municipalities were identified, with 1,916 affecting roadways and 2,596 affecting buildings. Most ADAs exhibited vertical movement due to land subsidence, while horizontal movements, mainly linked to landslides, were also significant. The majority of ADAs showed moderate to low displacement rates (

The satellite initiative Gravity Recovery and Climate Experiment (GRACE) has been thoroughly investigated in recent years to monitor groundwater storage (GWS). Since 2002, GRACE has provided distinctive perspectives on fluctuations in Earth's gravity field. Changes in gravity over time serve as valuable indicators for deducing alterations in total terrestrial water storage (TWS), encompassing soil moisture, surface water, snow and ice, canopy interception, wet biomass, and groundwater.The importance of GRACE data in determining GWS holds crucial implications, particularly in regions with limited hydrogeological information. Numerous analyses published so far have consistently demonstrated a robust correlation between GWS derived from GRACE and measurements obtained directly from wells in extensive aquifers.The Global Land Data Assimilation System (GLDAS) combines satellite and in situ data with sophisticated land surface modeling and data assimilation techniques. In the NASA GLDAS System Version 2 (GLDAS-2), GRACE data, initially on a 1° global grid, is downscaled to a higher resolution of 0.25°. This extension of data covers a daily scale from 1948, effectively interpolating temporal gaps present in the GRACE dataset. Following the isolation of contributions to temporal mass changes, GLDAS furnishes daily time series for GWS, making the GWS data readily available for utilization.The present study adds to the existing body of knowledge by showcasing that GRACE is skilled at capturing regionally averaged seasonal variations in observed GWS at two Spanish detritic aquifers: Almonte- Marismas and Alto Guadalentín. Even in these cases, where the study area is relatively small compared to the broader GRACE track, there are good correlations between in situ and satellite information. On the other hand, this work aims to assess the efficacy of readily accessible GWS data obtained from the GLDAS web services. This will be accomplished through the validation of GWS ready-to-use product from GLDAS involving (i) an examination of its correlation with piezometric information in both confined and unconfined aquifers, and (ii) an evaluation of net groundwater recharge rates computed by using GWS data.