Automated Image Localization and Damage Level Evaluation for Rapid Post-Event Building Assessment
Abstract
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. An inability to document the images’ locations would hinder the analysis, organization, and documentation of these images as they lack sufficient spatial context. This problem becomes more urgent to solve for the inspection mission covering a large area, like a community. To address this issue, the objective of this research is to generate a tool to automatically process the image data collected during such a mission and provide the location of each image. Towards this goal, the following tasks are performed. First, I develop a methodology to localize images and link them to locations on a structural drawing (Task 1). Second, this methodology is extended to be able to process data collected from a large scale area, and perform indoor localization for images collected on each of the indoor floors of each individual building (Task 2). Third, I develop an automated technique to render the damage condition decision of buildings by fusing the image data collected within (Task 3). The methods developed through each task have been evaluated with data collected from real world buildings. This research may also lead to automated assessment of buildings over a large scale area.
Degree
Ph.D.
Advisors
Dyke, Purdue University.
Subject Area
Aerospace engineering|Applied Mathematics|Architecture|Artificial intelligence|Economics|Electrical engineering|Geophysical engineering|Geophysics|Mathematics
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