OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents

Recent progress in multimodal reasoning has enabled agentsthat interpret imagery, connect it with language, and execute struc-tured analytical tasks. Extending these capabilities to remote sensingremains challenging, as models must reason over spatial scale, geographicstructures, and multispectral indices while maintaining coherent multi-step logic. To address this gap, we introduce OpenEarthAgent, a unifiedframework for tool-augmented geospatial reasoning trained on satelliteimagery, natural-language queries, and structured reasoning traces. Be-yond serving as a benchmark, OpenEarthAgent establishes a cohesiveagentic architecture built around a unified executable tool registry andtrajectory-based policy learning. The framework standardizes heteroge-neous visual, spectral, GIS, and georeferenced raster operations undera consistent callable schema, enabling modular orchestration and deter-ministic execution. Training is performed via supervised fine-tuning onstructured reasoning trajectories with deterministic replay validation toensure executability and spatial correctness. The accompanying corpuscomprises 14,538 training and 1,169 evaluation instances with over 107Kreasoning steps, spanning urban, environmental, disaster, and infrastruc-ture domains and incorporating GIS operations alongside index analysessuch as NDVI, NBR, and NDBI. Grounded in explicit reasoning traces,the learned agent demonstrates structured reasoning, stable spatial un-derstanding, and interpretable tool-driven behavior across diverse EOscenarios. We report consistent improvements over a strong baseline andcompetitive performance against recent open and closed-source models.https://github.com/mbzuai-oryx/OpenEarthAgent.