用AI分析街景图自动评估建筑遗产价值，提升保护效率
Automated Building Heritage Assessment Using Street-Level Imagery
- 结合GPT图像理解与建筑登记数据，训练分类模型
- 综合数据下宏观F1达0.71，仅用GPT数据也达0.60
- 适合城市规划、文化遗产保护者快速评估建筑价值
建筑遗产价值的登记对防止翻新和能效改造中价值流失至关重要，但传统人工登记耗时耗力。本研究利用OpenAI的大型语言模型GPT分析建筑立面图像，识别文化价值特征，并结合建筑登记数据，训练机器学习模型对瑞典斯德哥尔摩的多户及非住宅建筑进行分类。与遗产专家建立的清单对比验证显示，融合登记数据与GPT提取特征时，宏观F1得分为0.71；仅使用GPT数据时为0.60。该方法可生成更高质量的数据集，支持决策制定。
原文摘要 · Abstract (English)
Registration of heritage values in buildings is important to safeguard heritage values that can be lost in renovation and energy efficiency projects. However, registering heritage values is a cumbersome process. Novel artificial intelligence tools may improve efficiency in identifying heritage values in buildings compared to costly and time-consuming traditional inventories. In this study, OpenAI's large language model GPT was used to detect various aspects of cultural heritage value in facade images. Using GPT derived data and building register data, machine learning models were trained to classify multi-family and non-residential buildings in Stockholm, Sweden. Validation against a heritage 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 methods presented can contribute to higher-quality datasets and support decision making.
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