Gao, Lu2023-01-112023-01-11May 20222022-07-22https://hdl.handle.net/10657/13306Civil infrastructure systems usually cover large land areas and require frequent condition inspection to maintain their service to the public. Traditional methods such as manual surveys or vehicle-based automated surveys of infrastructure conditions are usually labor-intensive and time- consuming. As a result of budget constraints, it is important to explore more cost-effective approaches for infrastructure monitoring and maintenance programs. Considering recent advances in remote sensing satellite systems and image processing algorithms, many satellite sensing platforms and sensors have been used to monitor infrastructure conditions and detect damages. The level of details that can be detected increases significantly with the increase of ground sample distance (GSD), which is around15 cm - 30 cm for high-resolution satellite images. This report focuses on reviewing existing studies on the usage of image processing and deep learning models for analyzing satellite images in infrastructure management, which includes monitoring the condition of infrastructure facilities, detecting damage, evaluating impacts of disasters, and analyzing the usage of infrastructure assets.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Satellite Image AnalysisInfrastructure Management2023-01-11Thesisborn digital