Latent infrastructure sensing: Repurposing existing sensing mechanisms for infrastructure Health monitoring

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Aging civil infrastructure presents growing risks to public safety and structural resilience. Although structural health monitoring (SHM) has proven highly effective in monitoring and assessing critical assets, its broader implementation is hindered by upfront costs and the practical difficulties of deploying dedicated monitoring systems. Meanwhile, today’s cities generate massive, continuous data streams from connected vehicles, mobile devices, satellites, surveillance cameras, fiber-optic cables, and many other systems, originally designed for purposes like transportation, utilities, and security. A growing body of research shows that such urban data sources can be repurposed to infer structural health conditions across infrastructure networks more practically, while also supporting the objectives of the Smart & Connected Communities and Infrastructure Systems and People paradigms. Despite increasing interest, this field has lacked a unifying identity and systematic structure. Thus, this article attempts to formalize this domain of repurposing existing sensing mechanisms for infrastructure health monitoring under the term Latent Infrastructure Sensing (LIS). “Latent” emphasizes that valuable structural health information is already embedded in everyday urban data flow but remains largely concealed/underutilized/unexplored. In this article, we define the conceptual boundaries of LIS, propose a general evaluation criterion grounded in Reliability, Effectiveness, Actionability, and Practicality, and introduce the LIS Intelligence Framework that traces raw latent data through processing stages to decision-ready insights. We further synthesize existing LIS research into distinct categories (with representative studies), including crowdsensing via vehicles and mobile devices, satellite interferometry, stationary camera analytics, and emerging infrastructure sensing using fiber-optic cables, while highlighting key methodological gaps as well as socio-technical and governance challenges. We also identify promising future opportunities, such as sensing enabled by delivery robots, electric vehicle charging roads, and smart home devices. Hence, this article lays the foundation for scalable, practical, and potentially cost-effective infrastructure health monitoring that leverages data already coursing through contemporary cities.

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