CrowdLIM: Crowdsourcing to enable lifecycle infrastructure management

Changes occur gradually in a structure over its lifecycle. Periodic inspection of each structure is needed to prevent sudden failures and determine repair priorities. Human observation, the predominant mechanism for such assessments, is time-consuming and expensive, and often lacks consistency. Crowdsourcing provides a new opportunity to gather numerous photos of certain structures from various viewpoints and at frequent intervals, potentially enabling remote visual assessment. In this study, we exploit state-of-art computer vision techniques to streamline structural inspection and support lifecycle assessment by using visual data collected from ordinary citizens. One major inherent challenge in the use of such data is that they include a significant amount of irrelevant information because they are not captured intended for inspection purposes. To address this challenge, we develop an automated method to filter out unnecessary portions of the images and extract highly relevant regions-of-interest for reliable inspection. The technical approach is demonstrated using a regional landmark structure, the Bell Tower in the Purdue University in the United States. A large volume of images is collected in a crowdsourcing manner in six periods over two years. Then, the method successfully localizes the crowdsourcing images and extracts the image areas corresponding to three target inspection regions in the structure.