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Evaluation of Different Machine Learning Methods and Deep-Learning Convolutional Neural Networks for Landslide Detection

机译：评估不同机器学习方法和深度学习卷积神经网络进行滑坡检测

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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.
机译：在全球范围内，尤其是在喜马拉雅山等易受灾地区，对详细而准确的滑坡图和清单的需求日益增长。大多数标准制图方法需要专业知识，监督和现场工作。在这项研究中，我们使用来自Rapid Eye卫星的光学数据和地形因素来分析机器学习方法的潜力，即人工神经网络（ANN），支持向量机（SVM）和随机森林（RF）以及不同深度的学习卷积神经网络（CNN）用于滑坡检测。我们使用两个训练区和一个测试区来独立评估尼泊尔发生高度滑坡的Rasuwa地区不同方法的效果。使用ANN，SVM和RF以及不同的CNN实例创建了20个不同的地图，并通过平均交叉相交（mIOU）和其他通用指标将其与广泛的田野调查结果进行了比较。对于仅使用光谱信息的小窗口尺寸的CNN，此准确性评估可产生78.26％mIOU的最佳结果。来自5 m数字高程模型的附加信息有助于区分人类住区和滑坡，但不会提高整体分类的准确性。 CNN不会自动胜过ANN，SVM和RF，尽管有时会声称这一点。相反，CNN的性能在很大程度上取决于其设计，即层深度，输入窗口大小和训练策略。在这里，我们得出的结论是，由于大多数研究人员要么在诸如Google TensorFlow之类的解决方案中使用预定义的参数，要么会以反复试验的方式应用不同的设置，因此CNN方法仍处于起步阶段。然而，如果更好地理解不同设计的效果，存在足够的训练样本，并且更好地了解人为增加现有样本数量的增强策略的效果，则深度学习可以在将来改善滑坡图。

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