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.

利用基于深度学习的计算机视觉方法，该研究使用 2008 年至 2021 年间的 1500 万街道图像来测量伦敦的变化，成功地识别了住房供应的变化，为城市规划和政策决策提供及时信息，以建设更宜居、公平和可持续发展的城市。