{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T01:39:36Z","timestamp":1773797976863,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2012,5,7]],"date-time":"2012-05-07T00:00:00Z","timestamp":1336348800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Geoinformation derived from Earth observation (EO) plays a key role for detecting, analyzing and monitoring landslides to assist hazard and risk analysis. Within the framework of the EC-GMES-FP7 project SAFER (Services and Applications For Emergency Response) a semi-automated object-based approach for landslide detection and classification has been developed. The method was applied to a case study in North-Western Italy using SPOT-5 imagery and a digital elevation model (DEM), including its derivatives slope, aspect, curvature and plan curvature. For the classification in the object-based environment spectral, spatial and morphological properties as well as context information were used. In a first step, landslides were classified on a coarse segmentation level to separate them from other features with similar spectral characteristics. Thereafter, the classification was refined on a finer segmentation level, where two categories of mass movements were differentiated: flow-like landslides and other landslide types. In total, an area of 3.77 km\u00b2 was detected as landslide-affected area, 1.68 km\u00b2 were classified as flow-like landslides and 2.09 km\u00b2 as other landslide types. The outcomes were compared to and validated by pre-existing landslide inventory data (IFFI and PAI) and an interpretation of PSI (Persistent Scatterer Interferometry) measures derived from ERS1\/2, ENVISAT ASAR and RADARSAT-1 data. The spatial overlap of the detected landslides and existing landslide inventories revealed 44.8% (IFFI) and 50.4% (PAI), respectively. About 32% of the polygons identified through OBIA are covered by persistent scatterers data.<\/jats:p>","DOI":"10.3390\/rs4051310","type":"journal-article","created":{"date-parts":[[2012,5,7]],"date-time":"2012-05-07T11:16:02Z","timestamp":1336389362000},"page":"1310-1336","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":130,"title":["A Semi-Automated Object-Based Approach for Landslide Detection Validated by Persistent Scatterer Interferometry Measures and Landslide Inventories"],"prefix":"10.3390","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9282-8072","authenticated-orcid":false,"given":"Daniel","family":"H\u00f6lbling","sequence":"first","affiliation":[{"name":"Z_GIS Centre for Geoinformatics, Salzburg University, Schillerstrasse 30, A-5020 Salzburg, Austria"}]},{"given":"Petra","family":"F\u00fcreder","sequence":"additional","affiliation":[{"name":"Z_GIS Centre for Geoinformatics, Salzburg University, Schillerstrasse 30, A-5020 Salzburg, Austria"}]},{"given":"Francesco","family":"Antolini","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, University of Firenze, Via La Pira 4, I-50121 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8134-1576","authenticated-orcid":false,"given":"Francesca","family":"Cigna","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, University of Firenze, Via La Pira 4, I-50121 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8684-7848","authenticated-orcid":false,"given":"Nicola","family":"Casagli","sequence":"additional","affiliation":[{"name":"Department of Earth Sciences, University of Firenze, Via La Pira 4, I-50121 Firenze, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0619-0098","authenticated-orcid":false,"given":"Stefan","family":"Lang","sequence":"additional","affiliation":[{"name":"Z_GIS Centre for Geoinformatics, Salzburg University, Schillerstrasse 30, A-5020 Salzburg, Austria"}]}],"member":"1968","published-online":{"date-parts":[[2012,5,7]]},"reference":[{"key":"ref_1","unstructured":"Guha-Sapir, D., Vos, F., Below, R., and Ponserre, S. 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