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Open AccessArticle

Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software

1
Friedrich Schiller University Jena, Department of Geography, Jena, Germany
2
AIT Austrian Institute of Technology GmbH, Center for Mobility Systems, Vienna, Austria
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 551; https://doi.org/10.3390/ijgi8120551
Received: 10 October 2019 / Revised: 12 November 2019 / Accepted: 30 November 2019 / Published: 2 December 2019
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
With the increased availability of high-resolution digital terrain models (HRDTM) generated using airborne light detection and ranging (LiDAR), new opportunities for improved mapping of geohazards such as landslides arise. While the visual interpretation of LiDAR, HRDTM hillshades is a widely used approach, the automatic detection of landslides is promising to significantly speed up the compilation of inventories. Previous studies on automatic landslide detection often used a combination of optical imagery and geomorphometric data, and were implemented in commercial software. The objective of this study was to investigate the potential of open source software for automated landslide detection solely based on HRDTM-derived data in a study area in Burgenland, Austria. We implemented a geographic object-based image analysis (GEOBIA) consisting of (1) the calculation of land-surface variables, textural features and shape metrics, (2) the automated optimization of segmentation scale parameters, (3) region-growing segmentation of the landscape, (4) the supervised classification of landslide parts (scarp and body) using support vector machines (SVM), and (5) an assessment of the overall classification performance using a landslide inventory. We used the free and open source data-analysis environment R and its coupled geographic information system (GIS) software for the analysis; our code is included in the Supplementary Materials. The developed approach achieved a good performance (κ = 0.42) in the identification of landslides.
Keywords: geographic object-based image analysis; GEOBIA; open source GIS; landslide detection; LiDAR; high-resolution digital terrain model; HRDTM; support vector machine; SVM geographic object-based image analysis; GEOBIA; open source GIS; landslide detection; LiDAR; high-resolution digital terrain model; HRDTM; support vector machine; SVM
MDPI and ACS Style

Knevels, R.; Petschko, H.; Leopold, P.; Brenning, A. Geographic Object-Based Image Analysis for Automated Landslide Detection Using Open Source GIS Software. ISPRS Int. J. Geo-Inf. 2019, 8, 551.

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