Street view images of houses in Cambridge, UK, identifying building features. Red represents region contributing most to the ’Hard-to-decarbonize’ identification. Blue represents low contribution. Credit: Ronita Bardhan
First of its kind AI-model can help policymakers efficiently identify and prioritize houses for retrofitting and other decarbonizing measures. Street view images of houses in Cambridge, UK, identifying building features. Red represents region contributing most to the 'Hard-to-decarbonize' identification. Blue represents low contribution. Credit: Ronita Bardhan This is the first time that AI has been trained to identify hard-to-decarbonize buildings Ronita Bardhan '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.
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