{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T10:54:49Z","timestamp":1792666489033,"version":"4.5.0"},"reference-count":77,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T00:00:00Z","timestamp":1649116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020\u20132021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory.<\/jats:p>","DOI":"10.3390\/rs14071748","type":"journal-article","created":{"date-parts":[[2022,4,5]],"date-time":"2022-04-05T11:26:07Z","timestamp":1649157967000},"page":"1748","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4291-4604","authenticated-orcid":false,"given":"Pierluigi","family":"Confuorto","sequence":"first","affiliation":[{"name":"Earth Science Department, University of Firenze, Via G. La Pira 4, 50121 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7711-8419","authenticated-orcid":false,"given":"Camilla","family":"Medici","sequence":"additional","affiliation":[{"name":"Earth Science Department, University of Firenze, Via G. La Pira 4, 50121 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2724-5641","authenticated-orcid":false,"given":"Silvia","family":"Bianchini","sequence":"additional","affiliation":[{"name":"Earth Science Department, University of Firenze, Via G. La Pira 4, 50121 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7539-5850","authenticated-orcid":false,"given":"Matteo","family":"Del Soldato","sequence":"additional","affiliation":[{"name":"Earth Science Department, University of Firenze, Via G. La Pira 4, 50121 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8930-5705","authenticated-orcid":false,"given":"Ascanio","family":"Rosi","sequence":"additional","affiliation":[{"name":"Earth Science Department, University of Firenze, Via G. La Pira 4, 50121 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6030-1046","authenticated-orcid":false,"given":"Samuele","family":"Segoni","sequence":"additional","affiliation":[{"name":"Earth Science Department, University of Firenze, Via G. La Pira 4, 50121 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8684-7848","authenticated-orcid":false,"given":"Nicola","family":"Casagli","sequence":"additional","affiliation":[{"name":"Earth Science Department, University of Firenze, Via G. La Pira 4, 50121 Firenze, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10346-021-01799-y","article-title":"Exploring a landslide inventory created by automated web data mining: The case of Italy","volume":"19","author":"Franceschini","year":"2022","journal-title":"Landslides"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lanari, R., Bonano, M., Casu, F., Luca, C.D., Manunta, M., Manzo, M., Onorato, G., and Zinno, I. (2020). Automatic generation of sentinel-1 continental scale DInSAR deformation time series through an extended P-SBAS processing pipeline in a cloud computing environment. Remote Sens., 12.","DOI":"10.3390\/rs12182961"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Crosetto, M., Solari, L., Mr\u00f3z, M., Balasis-Levinsen, J., Casagli, N., Frei, M., Oyen, A., Moldestad, D.A., Bateson, L., and Guerrieri, L. (2020). The evolution of wide-area dinsar: From regional and national services to the European ground motion service. Remote Sens., 12.","DOI":"10.3390\/rs12122043"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.rse.2017.07.017","article-title":"Analysis of surface deformations over the whole Italian territory by interferometric processing of ERS, Envisat and COSMO-SkyMed radar data","volume":"202","author":"Costantini","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"143","article-title":"A nation-wide system for landslide mapping and risk management in Italy: The second Not-ordinary Plan of Environmental Remote Sensing","volume":"63","author":"Paci","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Novellino, A., Cigna, F., Brahmi, M., Sowter, A., Bateson, L., and Marsh, S. (2017). Assessing the Feasibility of a National InSAR Ground Deformation Map of Great Britain with Sentinel-1. Geosciences, 7.","DOI":"10.3390\/geosciences7020019"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2017.05.015","article-title":"A Copernicus downstream-service for the nationwide monitoring of surface displacements in Germany","volume":"202","author":"Kalia","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10346-018-0954-8","article-title":"Investigating slow-moving landslides in the Zhouqu region of China using InSAR time series","volume":"15","author":"Zhang","year":"2018","journal-title":"Landslides"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2257","DOI":"10.1080\/19475705.2019.1690058","article-title":"A Sentinel-1-based clustering analysis for geo-hazards mitigation at regional scale: A case study in Central Italy","volume":"10","author":"Montalti","year":"2019","journal-title":"Geomat. Nat. Hazards Risk"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"111983","DOI":"10.1016\/j.rse.2020.111983","article-title":"InSAR-based detection method for mapping and monitoring slow-moving landslides in remote regions with steep and mountainous terrain: An application to Nepal","volume":"249","author":"Bekaert","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1080\/22797254.2020.1728582","article-title":"Urban subsidence monitoring by SBAS-InSAR technique with multi-platform SAR images: A case study of Beijing Plain, China","volume":"53","author":"Liu","year":"2020","journal-title":"Eur. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Meng, Q., Confuorto, P., Peng, Y., Raspini, F., Bianchini, S., Han, S., Liu, H., and Casagli, N. (2020). Regional recognition and classification of active loess landslides using two-dimensional deformation derived from Sentinel-1 interferometric radar data. Remote Sens., 12.","DOI":"10.3390\/rs12101541"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1007\/s10346-021-01654-0","article-title":"Semi-automated regional classification of the style of activity of slow rock-slope deformations using PS InSAR and SqueeSAR velocity data","volume":"18","author":"Crippa","year":"2021","journal-title":"Landslides"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bozzano, F., Mazzanti, P., Perissin, D., Rocca, A., De Pari, P., and Discenza, M.E. (2017). Basin Scale Assessment of Landslides Geomorphological Setting by Advanced InSAR Analysis. Remote Sens., 9.","DOI":"10.3390\/rs9030267"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.rse.2016.11.002","article-title":"Post-failure evolution analysis of a rainfall-triggered landslide by multi-temporal interferometry SAR approaches integrated with geotechnical analysis","volume":"188","author":"Confuorto","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.enggeo.2018.04.015","article-title":"Detection and displacement characterization of landslides using multi-temporal satellite SAR interferometry: A case study of Danba County in the Dadu River Basin","volume":"240","author":"Dong","year":"2018","journal-title":"Eng. Geol."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Ezquerro, P., Del Soldato, M., Solari, L., Tom\u00e1s, R., Raspini, F., Ceccatelli, M., Fern\u00e1ndez-Merodo, J.A., Casagli, N., and Herrera, G. (2020). Vulnerability assessment of buildings due to land subsidence using InSAR data in the ancient historical city of Pistoia (Italy). Sensors, 20.","DOI":"10.3390\/s20102749"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Tzouvaras, M., Danezis, C., and Hadjimitsis, D.G. (2020). Differential SAR Interferometry Using Sentinel-1 Imagery-Limitations in Monitoring Fast Moving Landslides: The Case Study of Cyprus. Geosciences, 10.","DOI":"10.3390\/geosciences10060236"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s12665-013-2422-z","article-title":"Radar interferometry techniques for the study of ground subsidence phenomena: A review of practical issues through cases in Spain","volume":"71","author":"Romero","year":"2014","journal-title":"Environ. Earth Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"677","DOI":"10.1007\/s12665-013-2990-y","article-title":"Time-series analysis of subsidence associated with rapid urbanization in Shanghai, China measured with SBAS InSAR method","volume":"72","author":"Dong","year":"2014","journal-title":"Environmental Earth Sciences"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.rse.2017.11.022","article-title":"Mapping landslide surface displacements with time series SAR interferometry by combining persistent and distributed scatterers: A case study of Jiaju landslide in Danba, China","volume":"205","author":"Dong","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Solari, L., Del Soldato, M., Raspini, F., Barra, A., Bianchini, S., Confuorto, P., Casagli, N., and Crosetto, M. (2020). Review of satellite interferometry for landslide detection in Italy. Remote Sens., 12.","DOI":"10.3390\/rs12081351"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2609","DOI":"10.1007\/s10346-021-01648-y","article-title":"On the applicability of satellite SAR interferometry to landslide hazards detection in hilly areas: A case study of Shuicheng, Guizhou in Southwest China","volume":"18","author":"Wang","year":"2021","journal-title":"Landslides"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TGRS.2011.2124465","article-title":"A new algorithm for processing interferometric data-stacks: SqueeSAR","volume":"49","author":"Ferretti","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3285","DOI":"10.1109\/JSTARS.2014.2322671","article-title":"SBAS-DInSAR parallel processing for deformation time-series computation","volume":"7","author":"Casu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-25369-w","article-title":"Continuous, semi-automatic monitoring of ground deformation using Sentinel-1 satellites","volume":"8","author":"Raspini","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Del Soldato, M., Solari, L., Raspini, F., Bianchini, S., Ciampalini, A., Montalti, R., Ferretti, A., Pellegrineschi, V., and Casagli, N. (2019). Monitoring ground instabilities using SAR satellite data: A practical approach. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8070307"},{"key":"ref_28","first-page":"102448","article-title":"Sentinel-1-based monitoring services at regional scale in Italy: State of the art and main findings","volume":"102","author":"Confuorto","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"940","DOI":"10.1016\/j.scitotenv.2018.12.217","article-title":"Assessment of urban flood susceptibility using semi-supervised machine learning model","volume":"659","author":"Zhao","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3745","DOI":"10.1007\/s12665-014-3661-3","article-title":"Landslide susceptibility mapping using ensemble bivariate and multivariate statistical models in Fayfa area, Saudi Arabia","volume":"73","author":"Youssef","year":"2015","journal-title":"Environ. Earth Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"103225","DOI":"10.1016\/j.earscirev.2020.103225","article-title":"Machine learning methods for landslide susceptibility studies: A comparative overview of algorithm performance","volume":"207","author":"Merghadi","year":"2020","journal-title":"Earth-Sci. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2815","DOI":"10.5194\/nhess-13-2815-2013","article-title":"Landslide susceptibility estimation by random forests technique: Sensitivity and scaling issues","volume":"13","author":"Catani","year":"2013","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.enggeo.2011.09.006","article-title":"Landslide susceptibility assessment using SVM machine learning algorithm","volume":"123","author":"Bajat","year":"2011","journal-title":"Eng. Geol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.geomorph.2004.09.025","article-title":"Artificial neural networks applied to landslide susceptibility assessment","volume":"66","author":"Ermini","year":"2005","journal-title":"Geomorphology"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1016\/j.scitotenv.2019.02.263","article-title":"Comparison of convolutional neural networks for landslide susceptibility mapping in Yanshan County, China","volume":"666","author":"Wang","year":"2019","journal-title":"Sci. Total. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1007\/s10346-012-0320-1","article-title":"Mapping landslide susceptibility with logistic regression, multiple adaptive regression splines, classification and regression trees, and maximum entropy methods: A comparative study","volume":"10","author":"Cuartero","year":"2013","journal-title":"Landslides"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Raso, E., Di Martire, D., Cevasco, A., Calcaterra, D., Scarpellini, P., and Firpo, M. (2020). Evaluation of prediction capability of the MaxEnt and Frequency Ratio methods for landslide susceptibility in the Vernazza catchment (Cinque Terre, Italy). Applied Geology, Springer.","DOI":"10.1007\/978-3-030-43953-8_18"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bianchini, S., Solari, L., Del Soldato, M., Raspini, F., Montalti, R., Ciampalini, A., and Casagli, N. (2019). Ground Subsidence Susceptibility (GSS) Mapping in Grosseto Plain (Tuscany, Italy) Based on Satellite InSAR Data Using Frequency Ratio and Fuzzy Logic. Remote Sens., 11.","DOI":"10.3390\/rs11172015"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hakim, W.L., Achmad, A.R., and Lee, C.W. (2020). Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data. Remote Sens., 12.","DOI":"10.3390\/rs12213627"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12665-019-8518-3","article-title":"Land subsidence susceptibility assessment using random forest machine learning algorithm","volume":"78","author":"Mohammady","year":"2019","journal-title":"Environ. Earth Sci."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, F., Meng, X., Zhang, Y., Chen, G., Su, X., and Yue, D. (2019). Landslide susceptibility mapping of karakorum highway combined with the application of SBAS-InSAR technology. Sensors, 19.","DOI":"10.3390\/s19122685"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.rse.2016.07.018","article-title":"Landslide susceptibility map refinement using PSInSAR data","volume":"184","author":"Ciampalini","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"105317","DOI":"10.1016\/j.catena.2021.105317","article-title":"Slow-moving landslide risk assessment combining Machine Learning and InSAR techniques","volume":"203","author":"Novellino","year":"2021","journal-title":"Catena"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2011.11.020","article-title":"A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery","volume":"118","author":"Duro","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1049","DOI":"10.1007\/s11069-019-03795-x","article-title":"Random forest and artificial neural networks in landslide susceptibility modeling: A case study of the F\u00e3o River Basin, Southern Brazil","volume":"99","author":"Ruiz","year":"2019","journal-title":"Nat. Hazards"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105972","DOI":"10.1016\/j.enggeo.2020.105972","article-title":"Assessment of landslide susceptibility mapping based on Bayesian hyperparameter optimization: A comparison between logistic regression and random forest","volume":"281","author":"Sun","year":"2021","journal-title":"Eng. Geol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Canavesi, V., Segoni, S., Rosi, A., Ting, X., Nery, T., Catani, F., and Casagli, N. (2020). Different approaches to use morphometric attributes in landslide susceptibility mapping based on meso-scale spatial units: A case study in Rio de Janeiro (Brazil). Remote Sens., 12.","DOI":"10.3390\/rs12111826"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1038\/252018a0","article-title":"Remnant arcs and marginal basins in the Cainozoic development of the Mediterranean","volume":"252","author":"Boccaletti","year":"1974","journal-title":"Nature"},{"key":"ref_50","unstructured":"Bortolotti, V. (1992). The Tuscany\u2013Emilian Apennine, BEMA Editrice."},{"key":"ref_51","unstructured":"Vai, F., and Martini, I.P. (2013). Anatomy of an Orogen: The Apennines and Adjacent Mediterranean Basins, Springer."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"853","DOI":"10.5194\/nhess-15-853-2015","article-title":"An operational landslide early warning system at regional scale based on space\u2013time-variable rainfall thresholds","volume":"15","author":"Segoni","year":"2015","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s10346-017-0861-4","article-title":"The new landslide inventory of Tuscany (Italy) updated with PS-InSAR: Geomorphological features and landslide distribution","volume":"15","author":"Rosi","year":"2018","journal-title":"Landslides"},{"key":"ref_54","first-page":"328","article-title":"Subsidence mapping at regional scale using persistent scatters interferometry (PSI): The case of Tuscany region (Italy)","volume":"52","author":"Rosi","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Solari, L., Ciampalini, A., Raspini, F., Bianchini, S., Zinno, I., Bonano, M., Manunta, M., Moretti, S., and Casagli, N. (2017). Combined Use of C-and X-Band SAR Data for Subsidence Monitoring in an Urban Area. Geosciences, 7.","DOI":"10.3390\/geosciences7020021"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Del Soldato, M., Farolfi, G., Rosi, A., Raspini, F., and Casagli, N. (2018). Subsidence Evolution of the Firenze-Prato-Pistoia Plain (Central Italy) Combining PSI and GNSS Data. Remote Sens., 10.","DOI":"10.3390\/rs10071146"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s10040-020-02255-2","article-title":"Numerical modelling of land subsidence related to groundwater withdrawal in the Firenze-Prato-Pistoia basin (central Italy)","volume":"29","author":"Ceccatelli","year":"2021","journal-title":"Hydrogeol. J."},{"key":"ref_58","first-page":"1","article-title":"Digital terrain analysis","volume":"6","author":"Wilson","year":"2000","journal-title":"Terrain Anal. Princ. Appl."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"6623","DOI":"10.1080\/01431161.2012.693646","article-title":"Pre-survey suitability evaluation of the differential synthetic aperture radar interferometry method for landslide monitoring","volume":"33","author":"Plank","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2186","DOI":"10.1080\/01431161.2014.889864","article-title":"A methodology for improving landslide PSI data analysis","volume":"35","author":"Notti","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.5194\/nhess-10-1865-2010","article-title":"Assessment of the performance of X-band satellite radar data for landslide mapping and monitoring: Upper Tena Valley case study","volume":"10","author":"Notti","year":"2010","journal-title":"Nat. Hazards Earth Syst. Sci."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Del Soldato, M., Solari, L., Novellino, A., Monserrat, O., and Raspini, F. (2021). A new set of tools for the generation of InSAR visibility maps over wide areas. Geosciences, 11.","DOI":"10.3390\/geosciences11060229"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Evans, J.S., Murphy, M.A., Holden, Z.A., and Cushman, S.A. (2011). Modeling species distribution and change using random forest. Predictive Species and Habitat Modeling in Landscape Ecology, Springer.","DOI":"10.1007\/978-1-4419-7390-0_8"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.geomorph.2015.06.001","article-title":"Comparison of Logistic Regression and Random Forests techniques for shallow landslide susceptibility assessment in Giampilieri (NE Sicily, Italy)","volume":"249","author":"Trigila","year":"2015","journal-title":"Geomorphology"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Park, S., and Kim, J. (2019). Landslide susceptibility mapping based on random forest and boosted regression tree models, and a comparison of their performance. Appl. Sci., 9.","DOI":"10.3390\/app9050942"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.earscirev.2018.03.001","article-title":"A review of statistically-based landslide susceptibility models","volume":"180","author":"Reichenbach","year":"2018","journal-title":"Earth-Sci. Rev."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1007\/s10346-019-01299-0","article-title":"A step beyond landslide susceptibility maps: A simple method to investigate and explain the different outcomes obtained by different approaches","volume":"17","author":"Xiao","year":"2020","journal-title":"Landslides"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1111\/ecog.01881","article-title":"sdm: A reproducible and extensible R platform for species distribution modelling","volume":"39","author":"Naimi","year":"2016","journal-title":"Ecography"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1111\/j.1365-2664.2006.01214.x","article-title":"Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS)","volume":"43","author":"Allouche","year":"2006","journal-title":"J. Appl. Ecol."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1016\/j.catena.2015.10.010","article-title":"Application of GIS-based data driven random forest and maximum entropy models for groundwater potential mapping: A case study at Mehran Region, Iran","volume":"137","author":"Rahmati","year":"2016","journal-title":"Catena"},{"key":"ref_71","unstructured":"Trigila, A., Iadanza, C., and Guerrieri, L. (2007). The IFFI Project (Italian Landslide Inventory): Methodology and Results, ISPRA."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s10346-010-0213-0","article-title":"Quality assessment of the Italian Landslide Inventory using GIS processing","volume":"7","author":"Trigila","year":"2010","journal-title":"Landslides"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"177","DOI":"10.3389\/feart.2018.00177","article-title":"From Picture to Movie: Twenty Years of Ground Deformation recording over Tuscany Region (Italy) with Satellite InSAR","volume":"6","author":"Bianchini","year":"2018","journal-title":"Front. Earth Sci."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10661-018-6992-9","article-title":"Land subsidence phenomena investigated by spatiotemporal analysis of groundwater resources, remote sensing techniques, and random forest method: The case of Western Thessaly, Greece","volume":"190","author":"Ilia","year":"2018","journal-title":"Environ. Monit. Assess."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Solari, L., Ciampalini, A., Raspini, F., Bianchini, S., and Moretti, S. (2016). PSInSAR Analysis in the Pisa Urban Area (Italy): A Case Study of Subsidence Related to Stratigraphical Factors and Urbanization. Remote Sens., 8.","DOI":"10.3390\/rs8020120"},{"key":"ref_76","first-page":"14","article-title":"Evaluation of subsidence induced by long-lasting buildings load using InSAR technique and geotechnical data: The case study of a Freight Terminal (Tuscany, Italy)","volume":"82","author":"Ciampalini","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"2033","DOI":"10.1007\/s10346-019-01249-w","article-title":"Persistent Scatterers continuous streaming for landslide monitoring and mapping: The case of the Tuscany region (Italy)","volume":"16","author":"Raspini","year":"2019","journal-title":"Landslides"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1748\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:48:48Z","timestamp":1760136528000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/7\/1748"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,5]]},"references-count":77,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14071748"],"URL":"https:\/\/doi.org\/10.3390\/rs14071748","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,5]]}}}