Abstract
Reconnaissance teams are charged with collecting perishable data after a natural disaster. In the field, these engineers typically record their observations through images. Each team takes many views of both exterior and interior buildings and frequently collects associated metadata that reflect information represented in images, such as global positioning system (GPS) devices, structural drawings, timestamp, and measurements. Large quantities of images with a wide variety of contents are collected. The window of opportunity is short, and engineers need to provide accurate and rich descriptions of such images before the details are forgotten. In this paper, an automated approach is developed to organize and document such scientific information in an efficient and rapid manner. Deep convolutional neural network algorithms were successfully implemented to extract robust features of key visual contents in the images. A schema is designed based on the realistic needs of field teams examining buildings. A significant number of images collected from past earthquakes were used to train robust classifiers to automatically classify the images. The classifiers and associated schema were used to automatically generate individual reports for buildings.
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Acknowledgments
The authors wish to acknowledge partial support from National Science Foundation under Grant Nos. NSF-1608762 and DGE-1333468, and the valuable image contributions from the Center for Earthquake Engineering and Disaster Data (CrEEDD) at Purdue University (datacenterhub.org), the EUCentre (Pavia, Italy), the Instituto de Ingenieria of UNAM (Mexico), FEMA, and the EERI collections. The authors also acknowledge the NVIDIA Corporation for the donation of a high-end GPU board.
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©2018 American Society of Civil Engineers.
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Received: Feb 14, 2018
Accepted: Aug 7, 2018
Published online: Dec 5, 2018
Published in print: Feb 1, 2019
Discussion open until: May 5, 2019
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