Rapid mapping of landslides that occur after an earthquake is important for rapid crisis management. In this study, experimental research was conducted on the size of the model area and the data types used in developing classifiers for the supervised classification approaches used in rapid landslide mapping. The Hokkaido Iburu earthquake zone that occurred on September 6, 2018, was selected as the study area. PlanetScope pre-event and post-event images and ALOS-PALSAR Digital Elevation Model (DEM) were used in the analysis processes. In this context, five model areas with different sizes and one test area were determined. Object-based image analysis (OBIA) was used as a landslide mapping approach. Random Forest classifier, which is a supervised classification algorithm, was performed in the mapping of image objects produced by the segmentation stage of OBIA. Two different data sets were created for landslide mapping: change-based dataset and post-event dataset. The change-based dataset is generated from change data such as the difference of normalized difference vegetation index (δNDVI), change detection Image (CDI), princiable component analysis (PCA), and Independent component analysis (ICA) which are used in change detection applications. The post-event dataset was created from data generated from post-event image bands. When the obtained results were examined, higher accuracy results were obtained with the post-event dataset. Increasing the size of the model area, in other words, increasing the training data slightly increases the accuracy of landslide mapping. However, a model area that represents the region to be mapped in small sizes to make rapid decisions provides a 94% F-measure accuracy for earthquake-triggered landslide detection.

快速绘制地震发生后的滑坡图对于快速管理危机非常重要。在这项研究中，对模型区域的大小和用于快速滑坡制图的监督分类方法的分类器的开发中使用的数据类型进行了实验研究。2018年9月6日发生的北海道伊布鲁地震带被选为研究区域。分析过程中使用了PlanetScope事前和事后图像以及ALOS-PALSAR数字高程模型（DEM）。在这种情况下，确定了五个具有不同大小的模型区域和一个测试区域。基于对象的图像分析（OBIA）被用作滑坡映射方法。随机森林分类器，它是一种监督分类算法，是在OBIA分割阶段产生的图像对象的映射中执行的。为滑坡测绘创建了两个不同的数据集：基于变化的数据集和事件后数据集。基于更改的数据集是根据更改数据生成的，这些数据包括用于更改检测的归一化差异植被指数（δNDVI）的差异，更改检测图像（CDI），重要成分分析（PCA）和独立成分分析（ICA）应用程序。事件后数据集是根据事件后图像波段生成的数据创建的。当检查获得的结果时，使用事件后数据集可以获得更高的准确性结果。增加模型区域的大小，换句话说，增加训练数据会稍微增加滑坡映射的准确性。然而，