Extracting and classifying spatial language terms from planning documents

Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs)present new opportunities to advance text-based analyses, particularly for identify-ing and disambiguating geospatial information expressed as natural language text.This study explores the capabilities of an LLM to identify, classify, and create map-based representations of geospatial expressions found in urban planning documents.As a profession concerned with the spatial arrangement and interrelationships ofhuman activities, land uses, and environmental systems, much of the regulatoryinformation, professional expertise, and local knowledge that urban planning en-codes as text in reports and plans is geographic in nature. Interpreting and cross-referencing the geographic information in these documents is challenging and timeconsuming for both domain professionals (e.g., planners, lawyers) and communitymembers. This paper presents an automated pipeline that was developed to assessan LLM’s ability to extract geospatial expressions, determine their relevant spatialscale (micro, meso, macro), and to identify urban features referenced by a specificdevelopment proposal. Three types of planning documents were examined from amid-sized city (Kitchener, Ontario, Canada), namely a City-authored Official Plan(OP), private consultants’ development proposal reports, and transcripts from pub-lic meetings. A custom Retrieval-Augmented Generation (RAG) model was createdto process text hierarchically, extracting explicit and implicit geospatial expressionswith varying accuracy (e.g., F1-scores ranging from 0.440 to 0.800 across sources). Amulti-agent architecture, leveraging tools like Nominatim and DuckDB, was used toconvert these expressions into SQL queries and map representations. Results revealdistinct patterns in expression types and scales across document types, with OPdocuments favoring macro-scale expressions, plan justification reports emphasizingmicro-scale details, and public meeting transcripts reflecting informal micro-to-mesoreferences. While LLMs demonstrate strong potential, challenges such as factual in-consistencies, attribute errors, and context limitations highlight the need for humanoversight and improved metadata integration. This work advances the understand-ing of LLMs in geospatial analysis and highlights the potential to enhance urbanplanning processes through automated interpretation of geographic expressions intext

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.