Next Article in Journal
Some Investigations on a Possible Relationship between Ground Deformation and Seismic Activity at Campi Flegrei and Ischia Volcanic Areas (Southern Italy)
Previous Article in Journal
New Sensitivity Indices of a 2D Flood Inundation Model Using Gauss Quadrature Sampling
Article Menu

Export Article

Open AccessArticle

Unsupervised Classification for Landslide Detection from Airborne Laser Scanning

Department of Civil Engineering, California State Polytechnic University, Pomona, CA 91768, USA
Department of Geography, University of California, Los Angeles, CA 90095, USA
Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales (IANIGLA)—CONICET, 5500 Mendoza, Argentina
Author to whom correspondence should be addressed.
Geosciences 2019, 9(5), 221;
Received: 22 March 2019 / Revised: 6 May 2019 / Accepted: 11 May 2019 / Published: 15 May 2019
PDF [4364 KB, uploaded 15 May 2019]


Landslides are natural disasters that cause extensive environmental, infrastructure and socioeconomic damage worldwide. Since they are difficult to identify, it is imperative to evaluate innovative approaches to detect early-warning signs and assess their susceptibility, hazard and risk. The increasing availability of airborne laser-scanning data provides an opportunity for modern landslide mapping techniques to analyze topographic signature patterns of landslide, landslide-prone and landslide scarred areas over large swaths of terrain. In this study, a methodology based on several feature extractors and unsupervised classification, specifically k-means clustering and the Gaussian mixture model (GMM) were tested at the Carlyon Beach Peninsula in the state of Washington to map slide and non-slide terrain. When compared with the detailed, independently compiled landslide inventory map, the unsupervised methods correctly classify up to 87% of the terrain in the study area. These results suggest that (1) landslide scars associated with past deep-seated landslides may be identified using digital elevation models (DEMs) with unsupervised classification models; (2) feature extractors allow for individual analysis of specific topographic signatures; (3) unsupervised classification can be performed on each topographic signature using multiple number of clusters; (4) comparison of documented landslide prone regions to algorithm mapped regions show that algorithmic classification can accurately identify areas where deep-seated landslides have occurred. The conclusions of this study can be summarized by stating that unsupervised classification mapping methods and airborne light detection and ranging (LiDAR)-derived DEMs can offer important surface information that can be used as effective tools for digital terrain analysis to support landslide detection. View Full-Text
Keywords: DEM; landslide; detection; feature extraction; LiDAR; K-means clustering; Gaussian mixture model (GMM) DEM; landslide; detection; feature extraction; LiDAR; K-means clustering; Gaussian mixture model (GMM)

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Tran, C.J.; Mora, O.E.; Fayne, J.V.; Lenzano, M.G. Unsupervised Classification for Landslide Detection from Airborne Laser Scanning. Geosciences 2019, 9, 221.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Geosciences EISSN 2076-3263 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top