Automated Recovery of Structural Drawing Images Collected from Postdisaster Reconnaissance

Abstract

A large volume of images is collected during postdisaster building reconnaissance. For both older and new buildings, the structural drawings are an essential record of the structural information needed to extract valuable lessons to improve future performance. With older construction, these drawings often need to be captured as multiple photographs, herein referred to as partial drawing images (PDIs), taken at a close distance to ensure critical details are legible. However, the ability to use PDIs is quite limited due to the time-consuming process of manually classifying such photographs and the challenge of identifying their spatial arrangement. The authors offer a new solution to automatically recover high-quality structural drawing images. First, PDIs are classified from a set of images collected using an image classification algorithm, called convolutional neural network. Then, using the structure-from-motion algorithm, the geometric relationship between each set of PDIs and a corresponding physical drawing are computed to identify their arrangement. Finally, high-quality full drawing images are reconstructed. The capabilities of the technique are demonstrated using real-world images gathered from past reconnaissance missions and newly collected PDIs.