Automated Building Heritage Assessment Using Street-Level Imagery
This addresses the need for cost-effective heritage assessment in building energy retrofits, though it is incremental as it applies existing AI tools to a specific domain.
The study tackled the problem of efficiently identifying cultural heritage values in buildings for energy conservation by using GPT to detect heritage aspects in facade images and machine learning models to classify buildings in Stockholm, achieving a macro F1-score of 0.71 with combined data and 0.60 with GPT-only data.
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.