About this Research Topic
As global urbanization accelerates and infrastructure development progresses rapidly, the monitoring of building health and damage assessment has become pivotal in ensuring the durability, safety, and sustainability of structures. Recent advancements in fields such as computer vision, deep learning, remote sensing, LiDAR scanning, and the avant-garde concept of digital twin technologies have profoundly refined our approaches to detection, monitoring, and damage assessment in buildings, roads, bridges, and tunnels alike. However, it is imperative to recognize that different infrastructural elements present unique characteristics under various damage scenarios. Consequently, the overall efficacy and adaptability of these methods call for further scholarly scrutiny.
This Research Topic is anchored in addressing the multifaceted challenges associated with the efficient and timely detection and monitoring of damages in urban infrastructure. The aim is not merely to document existing damages, but to innovatively predict and preempt potential vulnerabilities, thus enhancing the durability, safety, and sustainability of urban structures. Leveraging the breakthroughs in computer vision, deep learning, remote sensing, and digital twin technologies, we aspire to transition from a historically reactive stance to a forward-thinking, proactive approach to infrastructure health monitoring. This endeavor necessitates a focus on urban infrastructural elements, including buildings, roads, bridges, and tunnels, and the integration of emerging technologies, especially deep learning and knowledge graphs, to provide comprehensive solutions for the nuanced challenges faced in maintaining urban architectural integrity.
We encourage submissions that shed light on theoretical and methodological advancements in urban infrastructure health monitoring. Topics of interest include but are not limited to:
• Advanced Optical/Remote Sensing/LiDAR Techniques for Health Detection, Monitoring, and Assessment;
• Applications of Deep Learning in Building Detection;
• Image Processing Foundations for Emergency Event Response;
• Applications of 3D Imaging in evaluating Infrastructure Health Status;
• Applications of Digital Twin Technologies in Urban Health Monitoring.
This Research Topic is anchored in addressing the multifaceted challenges associated with the efficient and timely detection and monitoring of damages in urban infrastructure. The aim is not merely to document existing damages, but to innovatively predict and preempt potential vulnerabilities, thus enhancing the durability, safety, and sustainability of urban structures. Leveraging the breakthroughs in computer vision, deep learning, remote sensing, and digital twin technologies, we aspire to transition from a historically reactive stance to a forward-thinking, proactive approach to infrastructure health monitoring. This endeavor necessitates a focus on urban infrastructural elements, including buildings, roads, bridges, and tunnels, and the integration of emerging technologies, especially deep learning and knowledge graphs, to provide comprehensive solutions for the nuanced challenges faced in maintaining urban architectural integrity.
We encourage submissions that shed light on theoretical and methodological advancements in urban infrastructure health monitoring. Topics of interest include but are not limited to:
• Advanced Optical/Remote Sensing/LiDAR Techniques for Health Detection, Monitoring, and Assessment;
• Applications of Deep Learning in Building Detection;
• Image Processing Foundations for Emergency Event Response;
• Applications of 3D Imaging in evaluating Infrastructure Health Status;
• Applications of Digital Twin Technologies in Urban Health Monitoring.
Keywords: Computer Vison, Deep Learning, Remote sensing, Health and Damage Monitoring, Digital Twin
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
