This research was conducted by Jorrit Ypenda in collaboration with AI Team, Urban Innovation and R&D, City of Amsterdam.
Involved civil servants: Maarten Sukel & Cláudia Pinhão & Daan Bloembergen
Supervisors: Maarten Sukel & Hamed S. Alavi
MSc Thesis - UvA - Fence Detection in Amsterdam: Transparent Object Segmentation in Urban Context - Jorrit Ypenga
MSc Thesis by Jorrit Ypenga
Accessibility and safe movement in urban areas entail infrastructure that minimizes the risks for pedestrians and bikers with diverse levels of abilities. Recognizing and mapping unsafe areas can increase awareness among citizens and also inform city projects to improve their infrastructure.
This contribution presents an example in which the specific objective is to recognize the unprotected areas around the canals in the city of Amsterdam. This is accomplished through running image processing algorithms on 11K waterside panoramas taken from the city of Amsterdam open data portal. We created an annotated subset of 2K processed images for training and evaluation. This dataset debuts a novel pixel-level annotation style using multi-lines, which is found to generalize better than traditional polygon-like bounding boxes.
To determine the best inference practice, we compared the IoU and robustness of several existing segmentation frameworks. The best method achieves an IoU of 0.79. We discuss the broader application of the presented method for the problem of “transparent object detection in urban context” and show that it is feasible.