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Appl. Sci. 2017, 7(7), 730; doi:10.3390/app7070730

Optimized Neural Architecture for Automatic Landslide Detection from High‐Resolution Airborne Laser Scanning Data

1
Department of Civil Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Malaysia
2
School of Systems, Management and Leadership, Faculty of Engineering and Information Technology, University of Technology Sydney, Building 11, Level 06, 81 Broadway, Ultimo NSW 2007 (P.O. Box 123), Australia
*
Author to whom correspondence should be addressed.
Received: 29 June 2017 / Revised: 11 July 2017 / Accepted: 13 July 2017 / Published: 16 July 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
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Abstract

An accurate inventory map is a prerequisite for the analysis of landslide susceptibility, hazard, and risk. Field survey, optical remote sensing, and synthetic aperture radar techniques are traditional techniques for landslide detection in tropical regions. However, such techniques are time consuming and costly. In addition, the dense vegetation of tropical forests complicates the generation of an accurate landslide inventory map for these regions. Given its ability to penetrate vegetation cover, high-resolution airborne light detection and ranging (LiDAR) has been used to generate accurate landslide maps. This study proposes the use of recurrent neural networks (RNN) and multi-layer perceptron neural networks (MLP-NN) in landscape detection. These efficient neural architectures require little or no prior knowledge compared with traditional classification methods. The proposed methods were tested in the Cameron Highlands, Malaysia. Segmentation parameters and feature selection were respectively optimized using a supervised approach and correlation-based feature selection. The hyper-parameters of network architecture were defined based on a systematic grid search. The accuracies of the RNN and MLP-NN models in the analysis area were 83.33% and 78.38%, respectively. The accuracies of the RNN and MLP-NN models in the test area were 81.11%, and 74.56%, respectively. These results indicated that the proposed models with optimized hyper-parameters produced the most accurate classification results. LiDAR-derived data, orthophotos, and textural features significantly affected the classification results. Therefore, the results indicated that the proposed methods have the potential to produce accurate and appropriate landslide inventory in tropical regions such as Malaysia. View Full-Text
Keywords: landslide detection; LiDAR; recurrent neural networks (RNN); multi‐layer perceptron neural networks (MLP‐NN); GIS; remote sensing landslide detection; LiDAR; recurrent neural networks (RNN); multi‐layer perceptron neural networks (MLP‐NN); GIS; remote sensing
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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).

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MDPI and ACS Style

Mezaal, M.R.; Pradhan, B.; Sameen, M.I.; Mohd Shafri, H.Z.; Yusoff, Z.M. Optimized Neural Architecture for Automatic Landslide Detection from High‐Resolution Airborne Laser Scanning Data. Appl. Sci. 2017, 7, 730.

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