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.

This paper introduces a conditional generative adversarial network to redesign a street-level image of urban scenes by generating 1) an urban intervention policy, 2) an attention map that localises where intervention is needed, 3) a high-resolution street-level image (1024 X 1024 or 1536 X1536) after implementing the intervention. We also introduce a new dataset that comprises aligned street-level images of before and after urban interventions from real-life scenarios that make this research possible. The introduced method has been trained on different ranges of urban interventions applied to realistic images. The trained model shows strong performance in re-modelling cities, outperforming existing methods that apply image-to-image translation in other domains that is computed in a single GPU. This research opens the door for machine intelligence to play a role in re-thinking and re-designing the different attributes of cities based on adversarial learning, going beyond the mainstream of facial landmarks manipulation or image synthesis from semantic segmentation.

As more companies wade into the business of building artificial intelligence systems to help you drive (or do the driving for you), a startup founded by an ex-Apple computer vision specialist is open sourcing a huge dataset that can help them on their road to autonomy. Mapillary, a Swedish startup backed by Sequoia, Atomico and others that has built a database of 130 million images through crowdsourcing -- think open-source Street View -- is releasing a free dataset of 25,000 street-level images from 190 countries, with pixel-level annotations that can be used to train automotive AI systems. The Mapillary Vistas Dataset claims to be "the world's largest, most diverse dataset for object recognition on street-level imagery." As with the rest of Mapillary's photos, the startup builds its image database on top of Mapbox and OpenStreetMap maps. The dataset is free for both academic and commercial researchers, and if anyone wants to build the results into commercial products, they must pay a commercial license.

Engineers from Caltech have developed a method that uses data from satellite and street-level images, such as the ones that you can see in Google maps, to create automatically an inventory of street trees that cities may use to better manage urban forests. Their work is described in the proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition, which was held in Las Vegas this summer. "Cities have been surveying their tree populations for decades, but the process is very labor intensive. It usually involves hiring arborists to go out with GPS units to mark the location of each individual tree and identify its species," says senior author Pietro Perona, the Allen E. Puckett Professor of Electrical Engineering in the Division of Engineering and Applied Science. "For this reason, tree surveys are usually only done every 20 to 30 years, and a lot can change in that time."