Object-based classification of terrestrial laser scanning point clouds for landslide monitoring

Andreas Mayr, Martin Rutzinger, Magnus Bremer, S.J. Oude Elberink, Felix Stumpf, Clemens Geitner

Research output: Contribution to journalConference articleAcademicpeer-review

52 Citations (Scopus)
333 Downloads (Pure)


Terrestrial laser scanning (TLS) is often used to monitor landslides and other gravitational mass movements with high levels of geometric detail and accuracy. However, unstructured TLS point clouds lack semantic information, which is required to geomorphologically interpret the measured changes. Extracting meaningful objects in a complex and dynamic environment is challenging due to the objects' fuzziness in reality, as well as the variability and ambiguity of their patterns in a morphometric feature space. This work presents a point‐cloud‐based approach for classifying multitemporal scenes of a hillslope affected by shallow landslides. The 3D point clouds are segmented into morphologically homogeneous and spatially connected parts. These segments are classified into seven target classes (scarp, eroded area, deposit, rock outcrop and different classes of vegetation) in a two‐step procedure: a supervised classification step with a machine‐learning classifier using morphometric features, followed by a correction step based on topological rules. This improves the final object extraction considerably.
Original languageEnglish
Pages (from-to)377-397
JournalPhotogrammetric record
Issue number160
Publication statusPublished - 1 Dec 2017
Event2nd Virtual Geoscience Conference 2016 - Bergen, Norway
Duration: 21 Sept 201623 Sept 2016
Conference number: 2




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