After a disaster, teams of structural engineers collect vast amounts of images from damaged buildings to obtain new knowledge and extract lessons from the event. However, in many cases, the images collected are captured without sufficient spatial context. When damage is severe, it may be quite difficult to even recognize the building. Accessing images of the predisaster condition of those buildings is required to accurately identify the cause of the failure or the actual loss in the building. Here, to address this issue, we develop a method to automatically extract pre‐event building images from 360° panorama images (panoramas). By providing a geotagged image collected near the target building as the input, panoramas close to the input image location are automatically downloaded through street view services (e.g., Google or Bing in the United States). By computing the geometric relationship between the panoramas and the target building, the most suitable projection direction for each panorama is identified to generate high‐quality 2D images of the building. Region‐based convolutional neural networks are exploited to recognize the building within those 2D images. Several panoramas are used so that the detected building images provide various viewpoints of the building. To demonstrate the capability of the technique, we consider residential buildings in Holiday Beach in Rockport, Texas, United States, that experienced significant devastation in Hurricane Harvey in 2017. Using geotagged images gathered during actual postdisaster building reconnaissance missions, we verify the method by successfully extracting residential building images from Google Street View images, which were captured before the event.

中文翻译：

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从360°全景图像中自动提取建筑物图像，以进行灾后评估

灾难发生后，结构工程师团队从受损建筑物中收集了大量图像，以获取新知识并从事件中汲取教训。但是，在许多情况下，在没有足够空间上下文的情况下捕获所收集的图像。当损坏严重时，甚至很难识别建筑物。需要访问那些建筑物的灾前状况图像，以准确识别建筑物的故障原因或实际损失。在这里，为了解决这个问题，我们开发了一种从360°全景图像（全景图）中自动提取赛前建筑物图像的方法。通过提供在目标建筑物附近收集的经过地理标记的图像作为输入，可以通过街景服务自动下载接近输入图像位置的全景图（例如，Google或Bing在美国）。通过计算全景图和目标建筑物之间的几何关系，可以确定每个全景图最合适的投影方向，以生成建筑物的高质量2D图像。利用基于区域的卷积神经网络来识别那些2D图像中的建筑物。使用多个全景图，以便检测到的建筑物图像提供建筑物的各种视点。为了展示该技术的功能，我们考虑了美国德克萨斯州罗克波特假日海滩假日住宅楼，这些住宅楼在2017年遭受了哈维飓风的严重破坏。