Kristina Dabrock

Tim Johansson, Mikael Mangold, Kristina Dabrock et al.

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 no comprehensive 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 developing information on heritage values in the Swedish building stock. Buildings in street view images from all over Sweden (N=154 710) have been analysed using multimodal Large Language Models (LLM) to assess visible aspects indicative of heritage value. Zero-shot predictions by LLMs were used as a basis for identifying buildings with potential heritage values for 5.0 million square meters of heated floor area. In this paper, the results of the predictions and lessons learned are presented and related to the development of the Swedish Building Renovation Plan as part of governance. The problems with the method and potential improvements are discussed. Risks with authorities use of LLM-based data are addressed, with a focus on issues of transparency, error detection and sycophancy.

Kristina Dabrock, Tim Johansson, Anna Donarelli et al.

Detailed data is required to quantify energy conservation measures in buildings, such as envelop retrofits, without compromising cultural heritage. Novel artificial intelligence tools may improve efficiency in identifying heritage values in buildings compared to costly and time-consuming traditional inventories. In this study, the large language model GPT was used to detect various aspects of cultural heritage value in façade images. Using this data and building register data as features, machine learning models were trained to classify multi-family and non-residential buildings in Stockholm, Sweden. Validation against an expert-created inventory shows a macro F1-score of 0.71 using a combination of register data and features retrieved from GPT, and a score of 0.60 using only GPT-derived data. The presented methodology can contribute to a higher-quality database and thus support careful energy efficiency measures and integrated consideration of heritage value in large-scale energetic refurbishment scenarios.