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GeoFlow: Agentic Workflow Automation for Geospatial Tasks

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arxiv 2508.04719 v1 pith:6OOUMHKC submitted 2025-08-05 cs.AI cs.LG

GeoFlow: Agentic Workflow Automation for Geospatial Tasks

Amulya Bhattaram , Justin Chung , Stanley Chung , Ranit Gupta , Janani Ramamoorthy , Kartikeya Gullapalli , Diana Marculescu , Dimitrios Stamoulis This is my paper
classification cs.AI cs.LG
keywords agenticgeoflowgeospatialmethodtasksacrossagentapproaches
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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We present GeoFlow, a method that automatically generates agentic workflows for geospatial tasks. Unlike prior work that focuses on reasoning decomposition and leaves API selection implicit, our method provides each agent with detailed tool-calling objectives to guide geospatial API invocation at runtime. GeoFlow increases agentic success by 6.8% and reduces token usage by up to fourfold across major LLM families compared to state-of-the-art approaches.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems

    cs.CV 2026-01 unverdicted novelty 7.0

    The paper delivers the first comprehensive review and unified taxonomy of agentic AI in remote sensing, covering single-agent copilots, multi-agent systems, planning mechanisms, benchmarks, and a roadmap while noting ...

  2. CangLing-KnowFlow: A Unified Knowledge-and-Flow-fused Agent for Comprehensive Remote Sensing Applications

    cs.AI 2025-12 reject novelty 5.0

    CangLing-KnowFlow combines a procedural knowledge base, dynamic workflow repair, and memory to beat ReAct/Reflexion on remote-sensing workflow tasks, but the benchmark is drawn from the same tasks used to build its kn...