Summary of Automated Real-world Sustainability Data Generation From Images Of Buildings, by Peter J Bentley et al.
Automated Real-World Sustainability Data Generation from Images of Buildings
by Peter J Bentley, Soo Ling Lim, Rajat Mathur, Sid Narang
First submitted to arxiv on: 28 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed image-to-data method leverages Large Language Models (LLMs) with prompt engineering and domain knowledge to estimate building features relevant for sustainability calculations. By processing images of buildings, the model can accurately predict a range of features, surpassing human performance in a comparable task. The approach is demonstrated on 47 apartments, showcasing its potential for generating tailored recommendations for property owners. Furthermore, the method’s scalability is discussed. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research uses AI to figure out how to make buildings more sustainable without needing detailed information about the building itself. By looking at pictures of buildings, a special type of computer program can guess things like insulation levels and energy usage. The results are surprisingly good – better than what a human could do! This technology has the potential to help building owners make changes that will reduce their environmental impact. |
Keywords
» Artificial intelligence » Prompt
