# Automated Real-World Sustainability Data Generation from Images of Buildings

arXiv: https://arxiv.org/abs/2405.18064 · HTML: https://mlsanity.com/paper/2405.18064/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 28 May 2024.

## Builds on (most cited first, 4 of 4)

- [Generating Training Data with Language Models: Towards Zero-Shot Language Understanding](https://mlsanity.com/paper/2202.04538/) (2022)
- [ChartLlama: A Multimodal LLM for Chart Understanding and Generation](https://mlsanity.com/paper/2311.16483/) (2023)
- [SCAPE: Searching Conceptual Architecture Prompts using Evolution](https://mlsanity.com/paper/2402.00089/) (2024)
- [Address-Specific Sustainable Accommodation Choice Through Real-World Data Integration](https://mlsanity.com/paper/2405.12934/) (2024)

## Built on by (most cited first, 2 of 2)

- [CLEAR: Cue Learning using Evolution for Accurate Recognition Applied to Sustainability Data Extraction](https://mlsanity.com/paper/2501.18504/) (2025)
- [Situating AI Agents in their World: Aspective Agentic AI for Dynamic Partially Observable Information Systems](https://mlsanity.com/paper/2509.03380/) (2025)

## Abstract (arXiv)

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.