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Open AccessArticle

Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas

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Department of Geoinformatics–Z_GIS, University of Salzburg, Salzburg 5020, Austria
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Department of Remote Sensing and GIS, University of Tabriz, Tabriz 5166616471, Iran
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Mountain Societies Research Institute, University of Central Asia, 736000 Khorog, Tajikistan
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Sustainability Research Centre, University of the Sunshine Coast, Sunshine Coast, Queensland 4556, Australia
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Discipline of Geography and Spatial Sciences, University of Tasmania, Hobart 7005, Australia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2575; https://doi.org/10.3390/rs11212575
Received: 3 October 2019 / Revised: 29 October 2019 / Accepted: 31 October 2019 / Published: 2 November 2019
(This article belongs to the Special Issue Mass Movement and Soil Erosion Monitoring Using Remote Sensing)
Landslides represent a severe hazard in many areas of the world. Accurate landslide maps are needed to document the occurrence and extent of landslides and to investigate their distribution, types, and the pattern of slope failures. Landslide maps are also crucial for determining landslide susceptibility and risk. Satellite data have been widely used for such investigations—next to data from airborne or unmanned aerial vehicle (UAV)-borne campaigns and Digital Elevation Models (DEMs). We have developed a methodology that incorporates object-based image analysis (OBIA) with three machine learning (ML) methods, namely, the multilayer perceptron neural network (MLP-NN) and random forest (RF), for landslide detection. We identified the optimal scale parameters (SP) and used them for multi-scale segmentation and further analysis. We evaluated the resulting objects using the object pureness index (OPI), object matching index (OMI), and object fitness index (OFI) measures. We then applied two different methods to optimize the landslide detection task: (a) an ensemble method of stacking that combines the different ML methods for improving the performance, and (b) Dempster–Shafer theory (DST), to combine the multi-scale segmentation and classification results. Through the combination of three ML methods and the multi-scale approach, the framework enhanced landslide detection when it was tested for detecting earthquake-triggered landslides in Rasuwa district, Nepal. PlanetScope optical satellite images and a DEM were used, along with the derived landslide conditioning factors. Different accuracy assessment measures were used to compare the results against a field-based landslide inventory. All ML methods yielded the highest overall accuracies ranging from 83.3% to 87.2% when using objects with the optimal SP compared to other SPs. However, applying DST to combine the multi-scale results of each ML method significantly increased the overall accuracies to almost 90%. Overall, the integration of OBIA with ML methods resulted in appropriate landslide detections, but using the optimal SP and ML method is crucial for success. View Full-Text
Keywords: landslide mapping; object-based image analysis (OBIA); scale parameter (SP); Dempster–Shafer theory (DST); Planetscope landslide mapping; object-based image analysis (OBIA); scale parameter (SP); Dempster–Shafer theory (DST); Planetscope
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Tavakkoli Piralilou, S.; Shahabi, H.; Jarihani, B.; Ghorbanzadeh, O.; Blaschke, T.; Gholamnia, K.; Meena, S.R.; Aryal, J. Landslide Detection Using Multi-Scale Image Segmentation and Different Machine Learning Models in the Higher Himalayas. Remote Sens. 2019, 11, 2575.

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