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Publicly accessible data and computer vision for retrofit planning: evidence from London social housing

Incomplete and fragmented data is hampering systematic retrofit planning in the UK. In this paper, the authors investigated whether currently accessible property information and street-level imagery could help overcome this barrier. Their findings suggest that publicly accessible housing data and machine learning hold genuine promise for large-scale urban retrofit planning.

Luke Gooding / Published on 30 June 2026

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Citation

Roussel R., and Gooding, L. (2026). Publicly accessible data and computer vision for retrofit planning: evidence from London social housing. Energy and Buildings 367. https://doi.org/10.1016/j.enbuild.2026.117740.

Street level shot of a row of terraced houses in the London borough of Newham.

Computer visioning technology can use street level imagery to identify data like building age or wall construction type.

Photo: Busà Photography / Getty Images

Nearly 60% of buildings in the UK carry poor energy performance ratings. Retrofit interventions can improve buildings’ energy efficiency: a necessary intervention given that buildings account for approximately 34% of global CO2 emissions. However, incomplete and fragmented data is hampering systematic retrofit planning in the UK.

In this paper the authors explored what building and retrofit-related data could be inferred from data which is already available, such as Google Street View or Energy Performance Certificates. They then investigated whether these could be combined with computer visioning models to assess and categorize characteristics like wall construction classification (directly relevant for fabric-first retrofit work) and architectural style classification (serves as a useful proxy for the age of a building). They found that computer vision models performed well when tested on the datasets they were trained on.

The authors also convened a workshop to examine challenges surrounding building data collection and integration, with project partners, retrofit organizations and consultancies, an architectural practice  and local government officials. Participants shared that inconsistent data formats, limited inter-organizational data sharing and a lack of hybrid data gathering approaches hampered smooth retrofit planning.

The authors concluded that publicly accessible property data, street-level imagery and computer vision can assist with large-scale retrofit planning: they can generate useful evidence in early planning stages where information about large numbers of buildings is lacking, incomplete or difficult to integrate. However, practical application will require improved datasets, greater standardization, and continued human oversight. When handled well, publicly accessible data is a viable evidence-gathering resource for retrofit planning.

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SEI author

Luke Gooding

Research Associate

SEI York

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Energy and Buildings Open access
Topics and subtopics
Governance : Public policy
Related centres
SEI York
Regions
United Kingdom