The seismically induced ground failure is defined as any earthquake-generated process that leads to deformations within a soil medium, which in turn results in permanent horizontal or vertical displacements of the ground surface. As a result, relative movements occur on the ground and structures affected by these movements and thus they may be damaged. Determining earthquake-induced ground failure areas is important to carry out damage assessment studies more quickly and reliably and to prevent more destructive damages. Large earthquake-induced ground failure areas or limited access to the areas due to earthquake causes costly and unsafe fieldwork. Using satellite photographs, earthquake-induced ground failure areas can be easily and reliably detected and the fieldwork can be planned quickly. This study aimed at determining the postearthquake-induced ground failure areas and buildings or structures partially ruined (damaged) by using a deep learning-based object detection method, using Google Earth satellite images after an earthquake. The data set obtained after the earthquake occurred in the 2018 Palu region of Indonesia was used. This data set is divided into two parts for training and test areas. A descriptive approach is considered for detecting the earthquake-induced ground failure areas and damaged structures from collected images from Google Earth software using satellite photographs, using a pretrained Faster R-CNN. To demonstrate the effectiveness of the proposed method, the data set was first created with Google Earth Pro software and it was generated with 392 images for the earthquake-induced ground failure area and 223 images for the damaged area with a resolution of 1024 × 600 pixels. The analyses were carried out by taking into account different image scales. As a result of the analyses, it was concluded that the earthquake-induced ground failure effects (liquefied soil) and damaged structures can be detected to a large extent by using object detection-based deep learning methods.

地震引起的地面破坏定义为任何导致土壤介质变形的地震过程，从而导致地面的永久水平或垂直位移。结果，相对运动发生在地面和受这些运动影响的结构上，因此它们可能被损坏。确定地震引起的地面破坏区域对于更快速，更可靠地进行破坏评估研究并防止更多破坏性破坏至关重要。大型的地震引发的地面破坏区域或由于地震而导致的进入区域受限会导致昂贵且不安全的野外作业。使用卫星照片，可以轻松可靠地检测出地震引起的地面破坏区域，并可以快速计划野外工作。这项研究旨在通过使用基于深度学习的物体检测方法，利用地震后的Google Earth卫星图像，来确定地震后诱发的地面破坏区域和部分毁坏（损坏）的建筑物或结构。使用了印度尼西亚2018年帕卢地区地震发生后获得的数据集。该数据集分为训练和测试区域两部分。考虑使用一种描述性方法，使用预先训练的Faster R-CNN，使用卫星照片从Google Earth软件收集的图像中检测地震引起的地面破坏区域和受损结构。为了证明所提出方法的有效性，该数据集首先使用Google Earth Pro软件创建，并生成了392张地震引发的地面破坏区域图像和223张受损区域的图像，分辨率为1024×600像素。分析是通过考虑不同的图像比例来进行的。分析的结果表明，通过使用基于对象检测的深度学习方法，可以在很大程度上检测地震引起的地面破坏效应（液化土壤）和受损结构。