MSc Thesis - UvA - Unsupervised Object Discovery with Urban Context Priors - Andrea Lombardo

The global issue of urban accessibility, particularly sidewalk accessibility, significantly impacts
the quality of life for around 16% of the world’s population. Traditional data collection methods
for sidewalk assessment are laborious, costly, and time-consuming. Although supervised learning
offers cost-effective scalability, it requires large, high-quality and diverse training datasets,
which are scarce in the urban accessibility domain. To overcome these challenges, we explore
the potential of self-supervised techniques in a computer vision pipeline to localize obstacles on
sidewalks without relying on large-scale datasets. Our work presents a modular pipeline comprising
deep learning models for unsupervised object discovery and a semantic segmentation
module to differentiate urban context aspects. The inclusion of semantic information improves
the model’s performance across all evaluations. We introduce a novel qualitative evaluation
framework and a user-friendly Graphic User Interface (GUI) to facilitate human-in-the-loop
expert evaluations. Additionally, we demonstrate the pipeline’s robustness across diverse geographical
inputs. Our work contributes to AI-assisted urban accessibility by bridging the gap
between research and real-world applications.

This research was conducted by Andrea Lombardo in collaboration with the AI Team, Urban Innovation and R&D, City of Amsterdam.

Involved civil servants: Diederik Roijers

Supervisors: Tim Alpherts (UvA)

Image credits

Header image: Masks of objects on sidewalks