Identifying Sidewalks from Crowdsourced SVI
N. Singh (TU Delft - Architecture and the Built Environment)
H. Ledoux – Mentor (TU Delft - Architecture and the Built Environment)
L.R.N. Beuster – Mentor (TU Delft - Architecture and the Built Environment)
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Abstract
Sidewalks are fundamental urban infrastructure, yet large-scale, openly available geospatial sidewalk datasets remain scarce worldwide, hindering pedestrian routing, accessibility analysis, and urban planning applications. This thesis investigates the feasibility of transforming crowdsourced street view imagery from Mapillary into structured georeferenced sidewalk data using only openly licensed data and pre-trained models. Two complementary automated pipelines are developed and evaluated across three study areas in Amsterdam (Netherlands) and Boston (United States).
The first pipeline, Sidewalk Inventory Mapping, constructs a binary presence inventory (yes/no per roadside) by aggregating pre-computed semantic segmentation outputs and SfM-corrected camera metadata from the Mapillary API onto the OpenStreetMap road network, without any local model inference. The second pipeline, Sidewalk Geometry Reconstruction, downloads imagery and processes it locally using a vision foundation model (DINOv3) for semantic segmentation and monocular metric depth estimation (Depth Anything V3) to reconstruct sidewalk polygons and centerlines.
Results demonstrate that the inventory pipeline achieves algorithmic precision of 97.2% to 98.6% and algorithmic recall up to 86.5%, providing highly reliable sidewalk detection where imagery exists. The geometry reconstruction pipeline successfully produces sidewalk polygons but with limited spatial accuracy (IoU 0.100–0.329, width MAE 1.18–1.40 m), resulting from cascading errors in segmentation, depth estimation, and GPS positioning. Both pipelines are fundamentally bounded by Mapillary's spatial coverage, with system recall dropping substantially in areas lacking imagery. The inventory approach emerges as the practical, scalable solution for city-wide deployment, while the geometry reconstruction demonstrates technical feasibility but requires further refinement for production use. All outputs are compatible with open data standards and can support OpenStreetMap enrichment workflows.
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