Automated localization of urban drainage infrastructure from public-access street-level images

Comprehensive management of urban drainage network infrastructure is essential for sustaining the operation of these systems despite stresses from component deterioration, urban densification, and a predicted intensification of rainfall events. In this context, up-to-date and accurate urban drainage network data is key. However, such data is often absent, outdated, or incomplete. In this study, a new approach to localize manhole covers and storm drains, using deep learning to mine publicly available street-level images, is presented, tested, and assessed. Thus, the time-consuming and costly acquisition of the location of these system components can be avoided. The approach is evaluated using 5,000 high-resolution panoramas covering 500 km of public roads in Switzerland. The object detection approach proposed shows good performance and an improvement over state of the art image-based urban drainage infrastructure component detection. While the geographical localization of the detected objects still contains errors, the accuracy achieved is nevertheless sufficient for some applications, e.g. flood risk assessment.