  When data on building features is unavailable, the task of determining how to
improve that building in terms of carbon emissions becomes infeasible. We show
that from only a set of images, a Large Language Model with appropriate prompt
engineering and domain knowledge can successfully estimate a range of building
features relevant for sustainability calculations. We compare our novel
image-to-data method with a ground truth comprising real building data for 47
apartments and achieve accuracy better than a human performing the same task.
We also demonstrate that the method can generate tailored recommendations to
the owner on how best to improve their properties and discuss methods to scale
the approach.
