Machine learning with extensive labelled training samples (e.g., positive and negative data) has been received much attention to address earthquake-induced landslide susceptibility mapping (LSM). However, massive labelled training data required by machine learning, particularly the precise negative data (i.e., non-landslide area), cannot be easily and efficiently collected. To address this issue, this study presents a one class classifier based negative data generation method for rapid earthquake-induced LSM. First, an incomplete landslide inventory (i.e., positive data) was produced with the aid of change detection using before-and-after satellite images and Geographic Information System (GIS). Second, one class classifier was utilized to compute the probability of landslide occurrence based on the incomplete landslide inventory, followed by the negative data generation from the low landslide susceptibility areas. Third, positive data as well as the generated negative data (i.e., non-landslide) were compounded to train traditional binary classifier to produce final LSM. Experimental results suggest that the proposed method is capable to achieve a comparable result with methods using the complete landslide inventory and display a good correspondence with recent landslide events, making it a suitable method for rapid earthquake-induced LSM. The finding in this study would be useful in regional disaster planning and risk reduction.

中文翻译：

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基于一类分类器的负数据快速地震诱发滑坡敏感性测绘方法

带有大量标记训练样本（例如，正数和负数）的机器学习已引起人们的广泛关注，以解决地震引起的滑坡敏感性地图（LSM）。但是，机器学习所需的大量带标签的训练数据，尤其是精确的负数据（即非滑坡区域），无法轻松有效地收集。为了解决这个问题，本研究提出了一种基于一类分类器的负数据生成方法，用于快速地震诱发的LSM。首先，借助前后卫星图像和地理信息系统（GIS）的变化检测，得出了不完整的滑坡清单（即正数据）。其次，基于不完整的滑坡清单，利用一个分类器来计算滑坡发生的概率，其次是低滑坡敏感性地区的负数据生成。第三，将正数据以及生成的负数据（即非滑坡）进行组合，以训练传统的二进制分类器以生成最终的LSM。实验结果表明，所提出的方法能够与使用完整滑坡清单的方法取得可比的结果，并显示与近期滑坡事件的良好对应关系，使其成为快速地震诱发的LSM的合适方法。这项研究中的发现将对区域灾难规划和降低风险有用。实验结果表明，所提出的方法能够与使用完整滑坡清单的方法取得可比的结果，并显示与近期滑坡事件的良好对应关系，使其成为快速地震诱发的LSM的合适方法。这项研究中的发现将对区域灾难规划和降低风险有用。实验结果表明，所提出的方法能够与使用完整滑坡清单的方法取得可比的结果，并显示与近期滑坡事件的良好对应关系，使其成为快速地震诱发的LSM的合适方法。这项研究中的发现将对区域灾难规划和降低风险有用。