OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents
Akashah Shabbir, Muhammad Umer Sheikh, Muhammad Akhtar Munir, Hiyam Debary, Mustansar Fiaz, Muhammad Zaigham Zaheer, Paolo Fraccaro, Fahad Shahbaz Khan, Muhammad Haris Khan, Xiao Xiang Zhu, and Salman Khan

TL;DR
OpenEarthAgent introduces a unified, tool-augmented framework for geospatial reasoning on satellite imagery, enabling structured, interpretable analysis across diverse remote sensing tasks with improved performance.
Contribution
It presents a novel cohesive architecture and training methodology for geospatial agents, standardizing heterogeneous operations and leveraging structured reasoning trajectories.
Findings
Demonstrates structured reasoning and spatial understanding in geospatial tasks.
Achieves consistent improvements over baseline models.
Handles diverse EO scenarios with interpretable tool-driven behavior.
Abstract
Recent progress in multimodal reasoning has enabled agents that interpret imagery, connect it with language, and execute structured analytical tasks. Extending these capabilities to remote sensing remains challenging, as models must reason over spatial scale, geographic structures, and multispectral indices while maintaining coherent multi-step logic. To address this gap, we introduce \textit{OpenEarthAgent}, a unified framework for tool-augmented geospatial reasoning trained on satellite imagery, natural-language queries, and structured reasoning traces. Beyond serving as a benchmark, OpenEarthAgent establishes a cohesive agentic architecture built around a unified executable tool registry and trajectory-based policy learning. The framework standardizes heterogeneous visual, spectral, GIS, and georeferenced raster operations under a consistent callable schema, enabling modular…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMultimodal Machine Learning Applications · Constraint Satisfaction and Optimization · Geographic Information Systems Studies
