Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in the Western Ghats of India. We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions.

中文翻译：

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使用深度学习方法快速绘制由 2018 年极端季风降雨引发的西高止山脉（印度）滑坡

降雨引起的滑坡清单可以使用遥感和地形数据编制，使用传统或半自动监督方法收集。在这项研究中，我们使用 PlanetScope 图像和深度学习卷积神经网络 (CNN) 绘制了印度西高止山脉卡纳塔克邦 Kodagu 地区 2018 年降雨引发的滑坡的地图。我们使用四重交叉验证 (CV) 来选择训练和测试数据以去除模型的任何随机结果。地形坡度数据被用作辅助信息以提高模型的性能。使用带有光谱信息的坡度数据创建的最终滑坡清单图减少了误报，这有助于将滑坡区域与其他类似特征（如贫瘠土地和河床）区分开来。然而，虽然包括斜率数据并没有增加真阳性，但与仅使用光谱信息来训练模型相比，整体准确度更高。仅使用光学数据时，正确分类的滑坡值的平均准确度为 65.5%，而使用坡度数据则提高到 78%。本研究中提出的方法可以应用于其他滑坡易发地区，其结果可用于支持滑坡易发地区的减灾。