{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T20:55:08Z","timestamp":1775076908184,"version":"3.50.1"},"reference-count":239,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union Next-GenerationEU","award":["1243 2\/8\/2022"],"award-info":[{"award-number":["1243 2\/8\/2022"]}]},{"name":"European Union Next-GenerationEU","award":["PE0000005"],"award-info":[{"award-number":["PE0000005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>We conducted a systematic literature review of 105 landslide susceptibility studies in Italy from 1980 to 2023, retrieved from the Scopus database. We discovered that Italian researchers primarily focus on rainfall-induced landslides (86.67% of the articles), especially shallow and fast movements (60%), with 72% of studies conducted at the local scale, while regional and national-level studies are rare. The most common data sources include remote sensing images validated by field surveys and official data portals at the national or regional level. Data splitting usually follows a 70:30 ratio and 24 modelling techniques were identified, with logistic regression being historically prevalent, although machine learning methods have rapidly gained popularity. Italian studies used 97 predisposing factors, with slope angle (98.09%), lithology (89.52%), land use\/land cover (78.09%), and aspect (77.14%) being the most employed. This review also identifies and discusses a few less-used factors, like soil sealing, rainfall, NDVI, and proximity to faults, which showed promising results in experimental studies. Predisposing factors are generally selected by expert judgment, but methods for forward factors selection and collinearity tests are becoming more common. This review synthesizes current knowledge, pinpointing gaps, highlighting emerging methodologies, and suggesting future research directions for better integration of susceptibility studies with landslide risk management.<\/jats:p>","DOI":"10.3390\/rs16234491","type":"journal-article","created":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T03:50:57Z","timestamp":1733111457000},"page":"4491","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Insights Gained from the Review of Landslide Susceptibility Assessment Studies in Italy"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6030-1046","authenticated-orcid":false,"given":"Samuele","family":"Segoni","sequence":"first","affiliation":[{"name":"Department of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3073-4390","authenticated-orcid":false,"given":"Rajendran Shobha","family":"Ajin","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5411-4685","authenticated-orcid":false,"given":"Nicola","family":"Nocentini","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, Italy"},{"name":"Department of Geosciences, University of Padua (UNIPD), Via G. Gradenigo 6, 35131 Padua, Italy"}]},{"given":"Riccardo","family":"Fanti","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences (DST), University of Florence (UNIFI), Via G. La Pira 4, 50121 Florence, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"118453","DOI":"10.1109\/ACCESS.2024.3449447","article-title":"Contrastive Self-Supervised Learning for Globally Distributed Landslide Detection","volume":"12","author":"Ghorbanzadeh","year":"2024","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1007\/s10346-006-0036-1","article-title":"Global Landslide and Avalanche Hotspots","volume":"3","author":"Nadim","year":"2006","journal-title":"Landslides"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.5194\/nhess-18-2161-2018","article-title":"Global Fatal Landslide Occurrence from 2004 to 2016","volume":"18","author":"Froude","year":"2018","journal-title":"Nat. Hazards Earth Syst. 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