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  1. Collecting massive amounts of image data is a common way to record the post-event condition of buildings, to be used by engineers and researchers to learn from that event. Key information needed to interpret the image data collected during these reconnaissance missions is the location within the building where each image was taken. However, image localization is difficult in an indoor environment, as GPS is not generally available because of weak or broken signals. To support rapid, seamless data collection during a reconnaissance mission, we develop and validate a fully automated technique to provide robust indoor localization while requiring no prior information about the condition or spatial layout of an indoor environment. The technique is meant for large-scale data collection across multiple floors within multiple buildings. A systematic method is designed to separate the reconnaissance data into individual buildings and individual floors. Then, for data within each floor, an optimization problem is formulated to automatically overlay the path onto the structural drawings providing robust results, and subsequently, yielding the image locations. The end-to end technique only requires the data collector to wear an additional inexpensive motion camera, thus, it does not add time or effort to the current rapid reconnaissance protocol. As no prior information about the condition or spatial layout of the indoor environment is needed, this technique can be adapted to a large variety of building environments and does not require any type of preparation in the postevent settings. This technique is validated using data collected from several real buildings. 
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  2. null (Ed.)
    Image data remains an important tool for post-event building assessment and documentation. After each natural hazard event, significant efforts are made by teams of engineers to visit the affected regions and collect useful image data. In general, a global positioning system (GPS) can provide useful spatial information for localizing image data. However, it is challenging to collect such information when images are captured in places where GPS signals are weak or interrupted, such as the indoor spaces of buildings. The inability to document the images’ locations hinders the analysis, organization, and documentation of these images as they lack sufficient spatial context. In this work, we develop a methodology to localize images and link them to locations on a structural drawing. A stream of images can readily be gathered along the path taken through a building using a compact camera. These images may be used to compute a relative location of each image in a 3D point cloud model, which is reconstructed using a visual odometry algorithm. The images may also be used to create local 3D textured models for building-components-of-interest using a structure-from-motion algorithm. A parallel set of images that are collected for building assessment is linked to the image stream using time information. By projecting the point cloud model to the structural drawing, the images can be overlaid onto the drawing, providing clear context information necessary to make use of those images. Additionally, components- or damage-of-interest captured in these images can be reconstructed in 3D, enabling detailed assessments having sufficient geospatial context. The technique is demonstrated by emulating post-event building assessment and data collection in a real building. 
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  3. null (Ed.)
  4. Reconnaissance teams collect perishable data after each disaster to learn about building performance. However, often these large image sets are not adequately curated, nor do they have sufficient metadata (e.g., GPS), hindering any chance to identify images from the same building when collected by different reconnaissance teams. In this study, Siamese convolutional neural networks (S‐CNN) are implemented and repurposed to establish a building search capability suitable for post‐disaster imagery. This method can automatically rank and retrieve corresponding building images in response to a single query using an image. In the demonstration, we utilize real‐world images collected from 174 reinforced‐concrete buildings affected by the 2016 Southern Taiwan and the 2017 Pohang (South Korea) earthquake events. A quantitative performance evaluation is conducted by examining two metrics introduced for this application: Similarity Score (SS) and Similarity Rank (SR). 
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  5. Abstract

    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.

     
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