dorsal/arxiv
View SchemaUsing street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications
| Authors | Tim Johansson, Mikael Mangold, Kristina Dabrock, Anna Donarelli, Ingrid Campo-Ruiz |
|---|---|
| Categories | |
| ArXiv ID | 2601.06056vv1 |
| URL | https://arxiv.org/abs/2601.06056 |
| License | http://creativecommons.org/licenses/by/4.0/ |
Abstract
During 2025 and 2026, the Energy Performance of Buildings Directive is being implemented in the European Union member states, requiring all member states to have National Building Renovation Plans. In Sweden, there is a lack of a national register of buildings with heritage values. This is seen as a barrier for the analyses underlying the development of Building Renovation Plans by the involved Swedish authorities. The purpose of this research was to assist Swedish authorities in assigning heritage values to building in the Swedish building stock. As part of the analyses, buildings in street view images from all over Sweden (N=154 710) have been analysed using multimodal Large Language Models (LLM) to assess aspects of heritage value. Zero-shot predictions by LLMs were used as a basis to for identifying buildings with potential heritage values for 5.0 million square meters of heated floor area for the Swedish Building Renovation Plan. In this paper, the results of the predictions and lessons learnt are presented and related to the development of Swedish Building Renovation Plan as part of governance. Potential risks for authorities using LLM-based data are addressed, with a focus on issues of transparency, error detection and sycophancy.
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"abstract": "During 2025 and 2026, the Energy Performance of Buildings Directive is being implemented in the European Union member states, requiring all member states to have National Building Renovation Plans. In Sweden, there is a lack of a national register of buildings with heritage values. This is seen as a barrier for the analyses underlying the development of Building Renovation Plans by the involved Swedish authorities. The purpose of this research was to assist Swedish authorities in assigning heritage values to building in the Swedish building stock. As part of the analyses, buildings in street view images from all over Sweden (N=154 710) have been analysed using multimodal Large Language Models (LLM) to assess aspects of heritage value. Zero-shot predictions by LLMs were used as a basis to for identifying buildings with potential heritage values for 5.0 million square meters of heated floor area for the Swedish Building Renovation Plan. In this paper, the results of the predictions and lessons learnt are presented and related to the development of Swedish Building Renovation Plan as part of governance. Potential risks for authorities using LLM-based data are addressed, with a focus on issues of transparency, error detection and sycophancy.",
"arxiv_id": "2601.06056",
"authors": [
"Tim Johansson",
"Mikael Mangold",
"Kristina Dabrock",
"Anna Donarelli",
"Ingrid Campo-Ruiz"
],
"categories": [
"cs.CY",
"cs.AI",
"cs.CV"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "Using street view images and visual LLMs to predict heritage values for governance support: Risks, ethics, and policy implications",
"url": "https://arxiv.org/abs/2601.06056",
"version": "v1"
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