{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T14:40:31Z","timestamp":1773931231788,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"German Federal Ministry of Education and Research (BMBF)"},{"name":"Bavarian State Ministry of the Environment and Consumer Protection"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Landslides represent a significant natural hazard with wide-reaching impacts. Addressing the challenge of accurately detecting and monitoring landslides, this research introduces a novel approach that combines feature tracking with histogram analysis for efficient outlier removal. Distinct from existing methods, our approach leverages advanced histogram techniques to significantly enhance the accuracy of landslide detection, setting a new standard in the field. Furthermore, when tested on three different data sets, this method demonstrated a notable reduction in outliers by approximately 15 to 25 percent of all displacement vectors, exemplifying its effectiveness. Key to our methodology is a refined feature tracking process utilizing terrestrial laser scanners, renowned for their precision and detail in capturing surface information. This enhanced feature tracking method allows for more accurate and reliable landslide monitoring, representing a significant advancement in geospatial analysis techniques.<\/jats:p>","DOI":"10.3390\/rs16010138","type":"journal-article","created":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T09:35:21Z","timestamp":1703756121000},"page":"138","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["3D Landslide Monitoring in High Spatial Resolution by Feature Tracking and Histogram Analyses Using Laser Scanners"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-1552-7759","authenticated-orcid":false,"given":"Kourosh","family":"Hosseini","sequence":"first","affiliation":[{"name":"Chair of Engineering Geodesy, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany"}]},{"given":"Leonhard","family":"Reindl","sequence":"additional","affiliation":[{"name":"Chair of Engineering Geodesy, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany"}]},{"given":"Lukas","family":"Raffl","sequence":"additional","affiliation":[{"name":"Chair of Engineering Geodesy, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5466-7682","authenticated-orcid":false,"given":"Wolfgang","family":"Wiedemann","sequence":"additional","affiliation":[{"name":"Chair of Engineering Geodesy, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7966-4322","authenticated-orcid":false,"given":"Christoph","family":"Holst","sequence":"additional","affiliation":[{"name":"Chair of Engineering Geodesy, TUM School of Engineering and Design, Technical University of Munich, 80333 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106494","DOI":"10.1016\/j.enggeo.2021.106494","article-title":"A Multi-Feature Fusion Transfer Learning Method for Displacement Prediction of Rainfall Reservoir-Induced Landslide with Step-like Deformation Characteristics","volume":"297","author":"Long","year":"2022","journal-title":"Eng. 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