Self-supervised learning unveils urban change from street-level images

Cities around the world are grappling with multiple interconnected challenges, including population growth, shortage of affordable and decent housing, and the need for neighborhood improvements. Despite its critical importance for policy, our ability to effectively monitor and track urban change remains limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change, as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic pretrained embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions towards more liveable, equitable, and sustainable cities. •First application of unsupervised and self-supervised methods to measure neighborhood change from street-level images.•We adapt the Barlow Twins strategy to learn representations from street-level images, embedding features of urban structures.•We develop a method to detect relevant changes in urban structures by comparing learned features at different time points.•We demonstrate the feasibility of our method for tracking neighborhood-level urban change in London between 2008 and 2018.