用大模型自动评估城市项目是否符合可持续标准
Using Large Language Models for a standard assessment mapping for sustainable communities
- 基于ISO 37101标准设计提示词，用大模型分类城市项目
- 在527个巴黎项目和398个欧盟项目上验证，分类一致性强
- 适合想快速标准化评估的城建部门和政策制定者
本文提出一种新方法，利用大语言模型（LLMs）简化城市可持续性评估流程，将项目自动映射到ISO 37101标准中的六大可持续目标与十二项议题。研究基于标准定义构建定制提示词，应用于两个数据集：巴黎市民预算的527个项目与欧盟PROBONO项目中的398项活动。结果表明，该方法能快速且一致地对城市项目进行可持续性分类。相比传统人工评估，显著节省时间并提升一致性。该方法有助于打破城市规划中的信息孤岛，提供项目影响的全局视角。尽管如此，仍需人类专家解读结果，并关注伦理问题。本研究为人工智能在城市规划中的应用提供了新范式，推动标准化可持续框架在不同城市环境中的落地。
原文摘要 · Abstract (English)
This paper presents a new approach to urban sustainability assessment through the use of Large Language Models (LLMs) to streamline the use of the ISO 37101 framework to automate and standardise the assessment of urban initiatives against the six "sustainability purposes" and twelve "issues" outlined in the standard. The methodology includes the development of a custom prompt based on the standard definitions and its application to two different datasets: 527 projects from the Paris Participatory Budget and 398 activities from the PROBONO Horizon 2020 project. The results show the effectiveness of LLMs in quickly and consistently categorising different urban initiatives according to sustainability criteria. The approach is particularly promising when it comes to breaking down silos in urban planning by providing a holistic view of the impact of projects. The paper discusses the advantages of this method over traditional human-led assessments, including significant time savings and improved consistency. However, it also points out the importance of human expertise in interpreting results and ethical considerations. This study hopefully can contribute to the growing body of work on AI applications in urban planning and provides a novel method for operationalising standardised sustainability frameworks in different urban contexts.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。