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After a disaster, teams of structural engineers collect vast amounts ofimages from damaged buildings to obtain lessons and gain knowledge from theevent. Images of damaged buildings and components provide valuable evidence tounderstand the consequences on our structures. However, in many cases, imagesof damaged buildings are often captured without sufficient spatial context.Also, they may be hard to recognize in cases with severe damage. Incorporatingpast images showing a pre-disaster condition of such buildings is helpful toaccurately evaluate possible circumstances related to a building's failure. Oneof the best resources to observe the pre-disaster condition of the buildings isGoogle Street View. A sequence of 360 panorama images which are captured alongstreets enables all-around views at each location on the street. Once a userknows the GPS information near the building, all external views of the buildingcan be made available. In this study, we develop an automated technique toextract past building images from 360 panorama images serviced by Google StreetView. Users only need to provide a geo-tagged image, collected near the targetbuilding, and the rest of the process is fully automated. High-quality andundistorted building images are extracted from past panoramas. Since thepanoramas are collected from various locations near the building along thestreet, the user can identify its pre-disaster conditions from the full set ofexternal views.

Automated Detection of Pre-Disaster Building Images from Google Street View