{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T14:02:50Z","timestamp":1784037770052,"version":"3.55.0"},"reference-count":72,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T00:00:00Z","timestamp":1530230400000},"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>A detailed and state-of-the-art landslide inventory map including precise landslide location is greatly required for landslide susceptibility, hazard, and risk assessments. Traditional techniques employed for landslide detection in tropical regions include field surveys, synthetic aperture radar techniques, and optical remote sensing. However, these techniques are time consuming and costly. Furthermore, complications arise for the generation of accurate landslide location maps in these regions due to dense vegetation in tropical forests. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) is typically employed to generate accurate landslide maps. The object-based technique generally consists of many homogeneous pixels grouped together in a meaningful way through image segmentation. In this paper, in order to address the limitations of this approach, the final decision is executed using Dempster\u2013Shafer theory (DST) rule combination based on probabilistic output from object-based support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN) classifiers. Therefore, this research proposes an efficient framework by combining three object-based classifiers using the DST method. Consequently, an existing supervised approach (i.e., fuzzy-based segmentation parameter optimizer) was adopted to optimize multiresolution segmentation parameters such as scale, shape, and compactness. Subsequently, a correlation-based feature selection (CFS) algorithm was employed to select the relevant features. Two study sites were selected to implement the method of landslide detection and evaluation of the proposed method (subset \u201cA\u201d for implementation and subset \u201cB\u201d for the transferrable). The DST method performed well in detecting landslide locations in tropical regions such as Malaysia, with potential applications in other similarly vegetated regions.<\/jats:p>","DOI":"10.3390\/rs10071029","type":"journal-article","created":{"date-parts":[[2018,6,29]],"date-time":"2018-06-29T10:51:50Z","timestamp":1530269510000},"page":"1029","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":81,"title":["Improving Landslide Detection from Airborne Laser Scanning Data Using Optimized Dempster\u2013Shafer"],"prefix":"10.3390","volume":"10","author":[{"given":"Mustafa Ridha","family":"Mezaal","sequence":"first","affiliation":[{"name":"School of Systems, Management and Leadership, Faculty of Engineering and IT, University of Technology Sydney, CB11.06.106, 81 Broadway, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"School of Systems, Management and Leadership, Faculty of Engineering and IT, University of Technology Sydney, CB11.06.106, 81 Broadway, Ultimo, NSW 2007, Australia"},{"name":"Department of Energy and Mineral Resources Engineering, Choongmu-gwan, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hossein Mojaddadi","family":"Rizeei","sequence":"additional","affiliation":[{"name":"School of Systems, Management and Leadership, Faculty of Engineering and IT, University of Technology Sydney, CB11.06.106, 81 Broadway, Ultimo, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.geomorph.2006.09.023","article-title":"Comparing landslide inventory maps","volume":"94","author":"Galli","year":"2008","journal-title":"Geomorphology"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pradhan, B., and Mezaal, M.R. 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