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Proceedings Paper

The automatic detection of landslide features from LiDAR DEM using modifying contour connection method
Author(s): Kahn-Bao Wu
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Paper Abstract

The U.S. Geological Survey (USGS) pointed out that landslides caused a loss of billions and thousands of deaths each year all over the world. In Taiwan, because of vulnerable geological conditions, the invasion of typhoon in summer, and over 70% of mountain area in the whole island, landslides not only cause people’s property damage but also threaten personal safety. Current landslide mapping techniques include field inventorying, photogrammetry, and the use of digital elevation models (DEMs) to highlight regions of past instability. Most researchers focus on automatically producing landslide inventory maps by using different kinds of remote sensing data. However, not many researches pay attention to deepseated landslides. Nowadays, deep-seated landslides are detected by experts from DEMs and there are few methods for the automatic detection of deep-seated landslides. One of the landslide mapping methods, contour connection method (CCM), utilizes bare earth light detection and ranging (LiDAR) to consistently detect landslide deposits on a landscape scale in an automated manner. However, CCM with computational complexity not only requires user inputs but focuses on general landslide geometry. This paper focuses on the automatic detection of landslide features by modifying CCM. The detection results were evaluated by the ground truth and marked some features of landslides and high susceptibility areas. It shows that by using the proposed method, the accuracy will be the same as that obtained by using CCM, but with less computation time.

Paper Details

Date Published: 9 October 2018
PDF: 8 pages
Proc. SPIE 10790, Earth Resources and Environmental Remote Sensing/GIS Applications IX, 107900I (9 October 2018); doi: 10.1117/12.2324949
Show Author Affiliations
Kahn-Bao Wu, National Taiwan Univ. (Taiwan)

Published in SPIE Proceedings Vol. 10790:
Earth Resources and Environmental Remote Sensing/GIS Applications IX
Ulrich Michel; Karsten Schulz, Editor(s)

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