Abstract
There is a growing demand for detailed and accurate landslide maps and inventories around the globe, but particularly in hazard-prone regions such as the Himalayas. Most standard mapping methods require expert knowledge, supervision and fieldwork. In this study, we use optical data from the Rapid Eye satellite and topographic factors to analyze the potential of machine learning methods, i.e., artificial neural network (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection. We use two training zones and one test zone to independently evaluate the performance of different methods in the highly landslide-prone Rasuwa district in Nepal. Twenty different maps are created using ANN, SVM and RF and different CNN instantiations and are compared against the results of extensive fieldwork through a mean intersection-over-union (mIOU) and other common metrics. This accuracy assessment yields the best result of 78.26% mIOU for a small window size CNN, which uses spectral information only. The additional information from a 5 m digital elevation model helps to discriminate between human settlements and landslides but does not improve the overall classification accuracy. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed. Rather, the performance of CNNs strongly depends on their design, i.e., layer depth, input window sizes and training strategies. Here, we conclude that the CNN method is still in its infancy as most researchers will either use predefined parameters in solutions like Google TensorFlow or will apply different settings in a trial-and-error manner. Nevertheless, deep-learning can improve landslide mapping in the future if the effects of the different designs are better understood, enough training samples exist, and the effects of augmentation strategies to artificially increase the number of existing samples are better understood.
Highlights
Mass movements such as landslides are a major natural hazard in mountainous regions all over the world [1]
The described machine learning (ML) and convolution neural networks (CNNs) methods using all mentioned parameters were used on the study iTnhetwdoestcrraibiendinMgLzoanndesCaNnNd mteesttheoddsfoursianngoatlhl emreznotinoen.edFopraraalml teetesrtss,wwereeruesmedovonedthtehsotsuedydetected lanindstwlidoetroabinjeincgtszwonheischanwdetreestsemd faolrlearntohthaner7z0onpei.xFeolsr taollatecsctosu, wntefroermgoevoemd ethtroisceidneatceccuterdaclaiensdbsleidtwe een the fieoflidbewljedcowtrsokrwksahsmiacmhplpwelseesarenandsdmthtahelleesrasattethelallilntitee7i0mimapagigxeeerrlyys.. tFFooorractthchoeeuCCntNNfNNormmgeeettohhmoodedstsritcthheienooapcptcitumimraalcailtehtsrherbseehstowhlodelsednws tewhreeere used
More parameters were used for implementing the CNN methods: in addition to using both five and eight-layer training data sets, different input methods first with five spectral layers from the RapidEye images (R, G, B, NIR) and the normalized difference vegetation index (NDVI) and called the resulting maps ANN5, SVM5 and RF5
Summary
Mass movements such as landslides are a major natural hazard in mountainous regions all over the world [1]. Some knowledge-based methods are independent of the existence of landslide inventory data sets for the generation of hazard and risk maps, the resulting maps require accuracy assessment and sensitivity analysis steps and, need accurate inventory data sets [18]. Recurrent neural network (RNN) and multilayer perceptron neural network (MLP-NN) methods typically require an input data set from sources like orthophotos or LiDAR-derived data. This applies to the use of textural features for landslide detection as in the study by Mezaal et al [27]. Mezaal et al [9] optimized the performance of ML methods in landslide detection by using Dempster–Shafer theory (DST) based on the probabilistic output from object-based SVM, K-nearest neighbor (KNN) and RF methods
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