Adaptive and Cost-Effective Collection of High-Quality Data for Critical Infrastructure and Emergency Management in Smart Cities--Framework and Challenges

摘要

In future smart cities, many decision processes in critical infrastructure and emergency management will be based on machine learning techniques. One particular application will be the processing of large datasets of visual images for defect assessment where the data is collected by a swarm of mobile sensing agents (e.g., unmanned aerial vehicles). In this context, examples of defective regions are corrosion and cracks in buildings and facilities [1], and potholes on roads. A critical requirement for the success of such assessment processes is the reliable detection, quantification, and localization of defective regions. Furthermore, in such applications, the real-time assessment is often critical so that the swarm can decide regarding the optimum strategy and corresponding actions for effective data collection in unknown environments (e.g., robots that will be used for earthquake reconnaissance and rescue where they enter buildings whose plan is unknown to the robot). On the other hand, the reliability of the assessments requires data of good quality, since poor data may negatively affect the accuracy of classification and predictions, and consequently, may introduce additional costs and time overhead.