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From Data to Decisions: Streamlining Geospatial Operations with Multimodal GlobeFlowGPT

Published: 22 November 2024 Publication History

Abstract

As machine learning increasingly becomes a crucial tool for geospatial data analysis, finding and deploying a suitable model presents significant challenges, including the need for expertise in both programming and geospatial analysis, organizing data flow, and accurately assessing the results. To address these challenges, this paper introduces GlobeFlowGPT, a multimodal, chat-based framework designed to meet these demands by integrating domain-specific tools, machine learning models, Multimodal Large Language Models, and essential operational data. It leverages a Large Language Model orchestrator, facilitating complex geospatial tasks through a conversational interface. GlobeFlowGPT's flexible, containerized architecture allows for the rapid integration of cutting-edge models tailored for geospatial data, ensuring that the framework remains scalable and relevant amid ongoing technological advancements. We demonstrate the ability of our framework to streamline the analysis of geospatial data and expand the capabilities of modern MLLMs with complex geospatial machine learning models.

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      cover image ACM Conferences
      SIGSPATIAL '24: Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems
      October 2024
      743 pages
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      Publication History

      Published: 22 November 2024
      Received: 07 June 2024

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      Author Tags

      1. LLM Agent
      2. Multimodal LLM
      3. Satellite Imagery

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      SIGSPATIAL '24 Paper Acceptance Rate 37 of 122 submissions, 30%;
      Overall Acceptance Rate 257 of 1,238 submissions, 21%

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