Stalder, Steven; Volpi, Michele; Buttner, Nicolas; Law, Stephen; Harttgen, Kenneth; Suel, Esra; (2024) Self-supervised learning unveils urban change from street-level images. Computers, Environment and Urban Systems , 112 , Article 102156. 10.1016/j.compenvurbsys.2024.102156.

Preview

PDF Self-supervised_Suel.pdf - Published Version Download (7MB) | Preview

Abstract

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.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as JSONXMLCSV

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item