ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing

• URL: http://arxiv.org/abs/2410.12376v2
• Date: Wed, 23 Oct 2024 12:58:14 GMT
• Title: ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing
• Authors: Qingming Lin, Rui Hu, Huaxia Li, Sensen Wu, Yadong Li, Kai Fang, Hailin Feng, Zhenhong Du, Liuchang Xu,
• Abstract summary: We propose ShapefileGPT, an innovative framework powered by large language models (LLMs) ShapefileGPT utilizes a multi-agent architecture, in which the planner agent is responsible for task decomposition and supervision, while the worker agent executes the tasks. For evaluation, we developed a benchmark dataset based on authoritative textbooks, encompassing tasks in categories such as geometric operations and spatial queries.
• Score: 8.594821438139187
• License:
• Abstract: Vector data is one of the two core data structures in geographic information science (GIS), essential for accurately storing and representing geospatial information. Shapefile, the most widely used vector data format, has become the industry standard supported by all major geographic information systems. However, processing this data typically requires specialized GIS knowledge and skills, creating a barrier for researchers from other fields and impeding interdisciplinary research in spatial data analysis. Moreover, while large language models (LLMs) have made significant advancements in natural language processing and task automation, they still face challenges in handling the complex spatial and topological relationships inherent in GIS vector data. To address these challenges, we propose ShapefileGPT, an innovative framework powered by LLMs, specifically designed to automate Shapefile tasks. ShapefileGPT utilizes a multi-agent architecture, in which the planner agent is responsible for task decomposition and supervision, while the worker agent executes the tasks. We developed a specialized function library for handling Shapefiles and provided comprehensive API documentation, enabling the worker agent to operate Shapefiles efficiently through function calling. For evaluation, we developed a benchmark dataset based on authoritative textbooks, encompassing tasks in categories such as geometric operations and spatial queries. ShapefileGPT achieved a task success rate of 95.24%, outperforming the GPT series models. In comparison to traditional LLMs, ShapefileGPT effectively handles complex vector data analysis tasks, overcoming the limitations of traditional LLMs in spatial analysis. This breakthrough opens new pathways for advancing automation and intelligence in the GIS field, with significant potential in interdisciplinary data analysis and application contexts.

Related papers

• GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis [0.0]
  Generative AI offers promising capabilities for spatial analysis. Despite their potential, the integration of generative AI with established GIS platforms remains underexplored. "GIS Copilot" allows GIS users to interact with QGIS using natural language commands for spatial analysis.
  arXiv  Detail & Related papers  (2024-11-05T15:53:59Z)
• Web-Scale Visual Entity Recognition: An LLM-Driven Data Approach [56.55633052479446]
  Web-scale visual entity recognition presents significant challenges due to the lack of clean, large-scale training data. We propose a novel methodology to curate such a dataset, leveraging a multimodal large language model (LLM) for label verification, metadata generation, and rationale explanation. Experiments demonstrate that models trained on this automatically curated data achieve state-of-the-art performance on web-scale visual entity recognition tasks.
  arXiv  Detail & Related papers  (2024-10-31T06:55:24Z)
• Geo-FuB: A Method for Constructing an Operator-Function Knowledge Base for Geospatial Code Generation Tasks Using Large Language Models [0.5242869847419834]
  This study introduces a framework to construct such a knowledge base, leveraging geospatial script semantics. An example knowledge base, Geo-FuB, built from 154,075 Google Earth Engine scripts, is available on GitHub.
  arXiv  Detail & Related papers  (2024-10-28T12:50:27Z)
• An LLM Agent for Automatic Geospatial Data Analysis [5.842462214442362]
  Large language models (LLMs) are being used in data science code generation tasks. Their application to geospatial data processing is challenging due to difficulties in incorporating complex data structures and spatial constraints. We introduce GeoAgent, a new interactive framework designed to help LLMs handle geospatial data processing more effectively.
  arXiv  Detail & Related papers  (2024-10-24T14:47:25Z)
• BabelBench: An Omni Benchmark for Code-Driven Analysis of Multimodal and Multistructured Data [61.936320820180875]
  Large language models (LLMs) have become increasingly pivotal across various domains. BabelBench is an innovative benchmark framework that evaluates the proficiency of LLMs in managing multimodal multistructured data with code execution. Our experimental findings on BabelBench indicate that even cutting-edge models like ChatGPT 4 exhibit substantial room for improvement.
  arXiv  Detail & Related papers  (2024-10-01T15:11:24Z)
• An Autonomous GIS Agent Framework for Geospatial Data Retrieval [0.0]
  This study proposes an autonomous GIS agent framework capable of retrieving required geospatial data. We developed a prototype agent based on the framework, released as a QGIS plugin (GeoData Retrieve Agent) and a Python program. Experiment results demonstrate its capability of retrieving data from various sources including OpenStreetMap, administrative boundaries and demographic data from the US Census Bureau.
  arXiv  Detail & Related papers  (2024-07-13T14:23:57Z)
• GeoGPT: Understanding and Processing Geospatial Tasks through An Autonomous GPT [6.618846295332767]
  Decision-makers in GIS need to combine a series of spatial algorithms and operations to solve geospatial tasks. We develop a new framework called GeoGPT that can conduct geospatial data collection, processing, and analysis in an autonomous manner.
  arXiv  Detail & Related papers  (2023-07-16T03:03:59Z)
• MGeo: Multi-Modal Geographic Pre-Training Method [49.78466122982627]
  We propose a novel query-POI matching method Multi-modal Geographic language model (MGeo) MGeo represents GC as a new modality and is able to fully extract multi-modal correlations for accurate query-POI matching. Our proposed multi-modal pre-training method can significantly improve the query-POI matching capability of generic PTMs.
  arXiv  Detail & Related papers  (2023-01-11T03:05:12Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• KILT: a Benchmark for Knowledge Intensive Language Tasks [102.33046195554886]
  We present a benchmark for knowledge-intensive language tasks (KILT) All tasks in KILT are grounded in the same snapshot of Wikipedia. We find that a shared dense vector index coupled with a seq2seq model is a strong baseline.
  arXiv  Detail & Related papers  (2020-09-04T15:32:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.