The Big Data group applies deep learning methods to various big data sources, especially high resolution satellite data and street imagery, to characterise urban environmental features and exposures, including housing, the social environment, air quality, noise and the transportation environment (mode, street safety, etc.) and to evaluate how urban land use and service delivery policies could impact health. The group also advances methodologies for the use of big data, such as how best to combine satellite and street level images, transferability of prediction models trained in one city to others, and the use of images to evaluate temporal change.
Changes in life expectancy and house prices in London from 2002 to 2019: hyper-resolution spatiotemporal analysis of death registration and real estate data
The Lancet Regional Health - Europe, 27, pp. 100580, 2023.
Characterisation of urban environment and activity across space and time using street images and deep learning in Accra
Scientific Reports, 12, iss. 1, pp. 20470, 2022.
Predicting air pollution spatial variation with street-level imagery
Remote Sensing, 14, iss. 14, pp. 3429, 2022.
A deep learning approach for meter-scale air quality estimation in urban environments using very high-spatial-resolution satellite imagery
Atmosphere, 13, iss. 5, pp. 696, 2022.
Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas
Remote Sensing of Environment, 257, pp. 112339, 2021.