Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
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 However, such data is often absent, outdated, or incomplete.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.
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 However, such data is often absent, outdated, or incomplete.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.
Automated localization of urban drainage infrastructure from public-access street-level images
Boller, Dominik (Autor:in) / Moy de Vitry, Matthew (Autor:in) / D. Wegner, Jan (Autor:in) / Leitão, João P. (Autor:in)
Urban Water Journal ; 16 ; 480-493
09.08.2019
14 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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