An image embedding-based approach for classifying street networks
F.O. Garrido Valenzuela (TU Delft - Transport and Logistics)
M.T.S. Lange (TU Delft - Transport and Planning)
Juan C. Herrera (Pontificia Universidad Católica de Chile)
S. van Cranenburgh (TU Delft - Transport and Logistics)
O. Cats (TU Delft - Transport and Planning)
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Abstract
We present a method to classify street networks using only geo-tagged street-level imagery. By combining pre-trained image embeddings with unsupervised clustering, it produces visually coherent street typologies without supervised training or labeled data and requires only minimal data curation. The approach is lightweight, scalable, and, in principle, transferable across urban contexts. In a Delft (Netherlands) case study, we classify approximately 2,000 road sections using over 70,000 images. Our method recovers distinct street types such as residential, arterial, and historic ones. These results show that pre-trained visual embeddings alone can support effective street classification from visual inputs, offering a practical tool for urban planning, transport analysis, and mobility research.
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