Using Large Language Models for a standard assessment mapping for sustainable communities
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
A high-resolution nationwide urban village mapping product for 342 Chinese cities based on foundation models
Urban Villages (UVs) represent a distinctive form of high-density informal settlement embedded within China's rapidly urbanizing cities. Accurate identification of UVs is critical for urban governance, renewal, and susta…
Language Based Mapping of Science Assessment Items to Skills
Knowledge of the association between assessment questions and the skills required to solve them is necessary for analysis of student learning. This association, often represented as a Q-matrix, is either hand-labeled by …
General Classificationtext-classificationText ClassificationEnvironmental large language model Evaluation (ELLE) dataset: A Benchmark for Evaluating Generative AI applications in Eco-environment Domain
Generative AI holds significant potential for ecological and environmental applications such as monitoring, data analysis, education, and policy support. However, its effectiveness is limited by the lack of a unified eva…
Language Model EvaluationLanguage ModelingLanguage ModellingLarge Language ModelIntegrating mobile and fixed monitoring data for high-resolution PM2.5 mapping using machine learning
Constructing high resolution air pollution maps at lower cost is crucial for sustainable city management and public health risk assessment. However, traditional fixed-site monitoring lacks spatial coverage, while mobile …
The role of artificial intelligence in achieving the Sustainable Development Goals
The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors across the society requires an assessment of its effect on sustainable development. Here we analyze published evidence of p…