Self-supervised learning unveils change in urban housing from street-level images
This provides timely information for urban planning and policy decisions to address housing shortages, though it is incremental as it adapts existing self-supervised methods to a specific domain.
The paper tackled the problem of monitoring urban housing changes by developing a self-supervised method called Street2Vec, which used 15 million street images from London (2008-2021) to identify point-level changes in housing supply and distinguish between major and minor changes without manual annotations.
Cities around the world face a critical shortage of affordable and decent housing. Despite its critical importance for policy, our ability to effectively monitor and track progress in urban housing is 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 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 toward more liveable, equitable, and sustainable cities.