Deep learning model trained to identify least green homes

Red represents region contributing most to the "Hard-to-decarbonize" identification. "Hard-to-decarbonize" (HtD) houses are responsible for over a quarter of all direct housing emissions – a major obstacle to achieving net zero – but are rarely identified or targeted for improvement. Now a new deep-learning model trained by researchers from Cambridge University's Department of Architecture promises to make it far easier, faster and cheaper to identify these high priority problem properties and develop strategies to improve their green credentials. Houses can be hard to decarbonize for various reasons including their age, structure, location, social-economic barriers and availability of data. Policymakers have tended to focus mostly on generic buildings or specific hard-to-decarbonise technologies but the study, published in the journal Sustainable Cities and Society, could help to change this.