Gespeichert in:
| Hauptverfasser: | , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2405.18064 |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866914926406139904 |
|---|---|
| author | Bentley, Peter J Lim, Soo Ling Mathur, Rajat Narang, Sid |
| author_facet | Bentley, Peter J Lim, Soo Ling Mathur, Rajat Narang, Sid |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_18064 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automated Real-World Sustainability Data Generation from Images of Buildings Bentley, Peter J Lim, Soo Ling Mathur, Rajat Narang, Sid Artificial Intelligence Computer Vision and Pattern Recognition 68T07, 94A08 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. |
| title | Automated Real-World Sustainability Data Generation from Images of Buildings |
| topic | Artificial Intelligence Computer Vision and Pattern Recognition 68T07, 94A08 |
| url | https://arxiv.org/abs/2405.18064 |