GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis

• URL: http://arxiv.org/abs/2411.03205v4
• Date: Fri, 22 Nov 2024 02:00:21 GMT
• Title: GIS Copilot: Towards an Autonomous GIS Agent for Spatial Analysis
• Authors: Temitope Akinboyewa, Zhenlong Li, Huan Ning, M. Naser Lessani,
• Abstract summary: 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.
• Score: 0.0
• License:
• Abstract: Recent advancements in Generative AI offer promising capabilities for spatial analysis. Despite their potential, the integration of generative AI with established GIS platforms remains underexplored. In this study, we propose a framework for integrating LLMs directly into existing GIS platforms, using QGIS as an example. Our approach leverages the reasoning and programming capabilities of LLMs to autonomously generate spatial analysis workflows and code through an informed agent that has comprehensive documentation of key GIS tools and parameters. The implementation of this framework resulted in the development of a "GIS Copilot" that allows GIS users to interact with QGIS using natural language commands for spatial analysis. The GIS Copilot was evaluated with over 100 spatial analysis tasks with three complexity levels: basic tasks that require one GIS tool and typically involve one data layer to perform simple operations; intermediate tasks involving multi-step processes with multiple tools, guided by user instructions; and advanced tasks which involve multi-step processes that require multiple tools but not guided by user instructions, necessitating the agent to independently decide on and executes the necessary steps. The evaluation reveals that the GIS Copilot demonstrates strong potential in automating foundational GIS operations, with a high success rate in tool selection and code generation for basic and intermediate tasks, while challenges remain in achieving full autonomy for more complex tasks. This study contributes to the emerging vision of Autonomous GIS, providing a pathway for non-experts to engage with geospatial analysis with minimal prior expertise. While full autonomy is yet to be achieved, the GIS Copilot demonstrates significant potential for simplifying GIS workflows and enhancing decision-making processes.

Related papers

• 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)
• ShapefileGPT: A Multi-Agent Large Language Model Framework for Automated Shapefile Processing [8.594821438139187]
  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.
  arXiv  Detail & Related papers  (2024-10-16T08:48:27Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• 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)
• GAIA Search: Hugging Face and Pyserini Interoperability for NLP Training Data Exploration [97.68234051078997]
  We discuss how Pyserini can be integrated with the Hugging Face ecosystem of open-source AI libraries and artifacts. We include a Jupyter Notebook-based walk through the core interoperability features, available on GitHub. We present GAIA Search - a search engine built following previously laid out principles, giving access to four popular large-scale text collections.
  arXiv  Detail & Related papers  (2023-06-02T12:09:59Z)
• Autonomous GIS: the next-generation AI-powered GIS [0.0]
  We introduce Autonomous GIS as an AI-powered geographic information system (GIS) We develop a prototype system called LLM-Geo using the GPT-4 API in a Python environment. For all case studies, LLM-Geo was able to return accurate results, including aggregated numbers, graphs, and maps.
  arXiv  Detail & Related papers  (2023-05-10T20:46:29Z)
• OpenAGI: When LLM Meets Domain Experts [51.86179657467822]
  Human Intelligence (HI) excels at combining basic skills to solve complex tasks. This capability is vital for Artificial Intelligence (AI) and should be embedded in comprehensive AI Agents. We introduce OpenAGI, an open-source platform designed for solving multi-step, real-world tasks.
  arXiv  Detail & Related papers  (2023-04-10T03:55:35Z)
• 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)
• Semantic Segmentation of Vegetation in Remote Sensing Imagery Using Deep Learning [77.34726150561087]
  We propose an approach for creating a multi-modal and large-temporal dataset comprised of publicly available Remote Sensing data. We use Convolutional Neural Networks (CNN) models that are capable of separating different classes of vegetation.
  arXiv  Detail & Related papers  (2022-09-28T18:51:59Z)
• AutoGeoLabel: Automated Label Generation for Geospatial Machine Learning [69.47585818994959]
  We evaluate a big data processing pipeline to auto-generate labels for remote sensing data. We utilize the big geo-data platform IBM PAIRS to dynamically generate such labels in dense urban areas.
  arXiv  Detail & Related papers  (2022-01-31T20:02:22Z)

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.