GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing

• URL: http://arxiv.org/abs/2604.08896v1
• Date: Fri, 10 Apr 2026 02:59:38 GMT
• Title: GeoMMBench and GeoMMAgent: Toward Expert-Level Multimodal Intelligence in Geoscience and Remote Sensing
• Abstract summary: GeoMMBench is a comprehensive multimodal question-answering benchmark covering diverse RS disciplines, sensors, and tasks.<n>Using GeoMMBench, we assess 36 open-source and proprietary large language models, uncovering systematic deficiencies in domain knowledge.<n>We propose GeoMMAgent, a multi-agent framework that strategically integrates retrieval, perception, and reasoning through domain-specific RS models and tools.
• Score: 26.030446937726893
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Recent advances in multimodal large language models (MLLMs) have accelerated progress in domain-oriented AI, yet their development in geoscience and remote sensing (RS) remains constrained by distinctive challenges: wide-ranging disciplinary knowledge, heterogeneous sensor modalities, and a fragmented spectrum of tasks. To bridge these gaps, we introduce GeoMMBench, a comprehensive multimodal question-answering benchmark covering diverse RS disciplines, sensors, and tasks, enabling broader and more rigorous evaluation than prior benchmarks. Using GeoMMBench, we assess 36 open-source and proprietary large language models, uncovering systematic deficiencies in domain knowledge, perceptual grounding, and reasoning--capabilities essential for expert-level geospatial interpretation. Beyond evaluation, we propose GeoMMAgent, a multi-agent framework that strategically integrates retrieval, perception, and reasoning through domain-specific RS models and tools. Extensive experimental results demonstrate that GeoMMAgent significantly outperforms standalone LLMs, underscoring the importance of tool-augmented agents for dynamically tackling complex geoscience and RS challenges.

Related papers

• GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis [16.604040127938955]
  We introduce GeoAgentBench (GABench), a dynamic and interactive evaluation benchmark tailored for tool-augmented GIS agents.<n>GABench provides a realistic sandbox integrating 117 atomic GIS tools, encompassing 53 typical spatial analysis tasks.<n>We develop a novel agent architecture, Plan-and-React, that mimics expert cognitive anomalies by decoupling global orchestration from step-wise reactive execution.
  arXiv  Detail & Related papers  (2026-04-15T13:55:34Z)
• OpenEarthAgent: A Unified Framework for Tool-Augmented Geospatial Agents [68.85365034738534]
  We introduce a unified framework for developing tool-augmented geospatial agents trained on satellite imagery, natural-language queries, and detailed reasoning traces.<n>The training pipeline relies on supervised fine-tuning over structured reasoning trajectories, aligning the model with verified multistep tool interactions.<n>The accompanying corpus comprises 14,538 training and 1,169 evaluation instances, with more than 100K reasoning steps in the training split and over 7K reasoning steps in the evaluation split.
  arXiv  Detail & Related papers  (2026-02-19T18:59:54Z)
• GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics [91.17301794848025]
  This paper presents GeoAgent, a model capable of reasoning closely with humans and deriving fine-grained address conclusions.<n>Previous RL-based methods have achieved breakthroughs in performance and interpretability but still remain concerns because of their reliance on AI-generated chain-of-thought (CoT) data and training strategies.
  arXiv  Detail & Related papers  (2026-02-13T04:48:05Z)
• Thinking with Geometry: Active Geometry Integration for Spatial Reasoning [68.59084007360615]
  We propose GeoThinker, a framework that shifts paradigm passive fusion to active perception.<n>Instead of feature mixing, GeoThinker enables the model to selectively retrieve geometric evidence conditioned on its internal reasoning demands.<n>Our results indicate that the ability to actively integrate spatial structures is essential for next-generation spatial intelligence.
  arXiv  Detail & Related papers  (2026-02-05T18:59:32Z)
• Earth-Agent: Unlocking the Full Landscape of Earth Observation with Agents [49.3216026940601]
  Earth observation is essential for understanding the states of the Earth system.<n>Recent MLLMs have advanced EO research, but they still lack the capability to tackle complex tasks that require multi-step reasoning.<n>We introduce Earth-Agent, the first agentic framework that unifies RGB and spectral EO data within an MCP-based tool ecosystem.
  arXiv  Detail & Related papers  (2025-09-27T06:04:28Z)
• GeoEvolve: Automating Geospatial Model Discovery via Multi-Agent Large Language Models [49.257706111340134]
  We introduce GeoEvolve, a multi-agent LLM framework that couples evolutionary search with geospatial domain knowledge.<n>We evaluate it on two fundamental and classical tasks: spatial (kriging) and spatial uncertainty.<n>It reduces spatial error (RMSE) by 13-21% and enhances uncertainty estimation performance by 17%.
  arXiv  Detail & Related papers  (2025-09-25T21:03:57Z)
• CREW-WILDFIRE: Benchmarking Agentic Multi-Agent Collaborations at Scale [4.464959191643012]
  We introduce CREW-Wildfire, an open-source benchmark designed to evaluate next-generation multi-agent Agentic AI frameworks.<n> CREW-Wildfire offers procedurally generated wildfire response scenarios featuring large maps, heterogeneous agents, partial observability, dynamics, and long-horizon planning objectives.<n>We implement and evaluate several state-of-the-art LLM-based multi-agent Agentic AI frameworks, uncovering significant performance gaps.
  arXiv  Detail & Related papers  (2025-07-07T16:33:42Z)
• OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence [51.0456395687016]
  multimodal large language models (LLMs) have opened new frontiers in artificial intelligence.<n>We propose a MLLM (OmniGeo) tailored to geospatial applications.<n>By combining the strengths of natural language understanding and spatial reasoning, our model enhances the ability of instruction following and the accuracy of GeoAI systems.
  arXiv  Detail & Related papers  (2025-03-20T16:45:48Z)
• Multi-Agent Geospatial Copilots for Remote Sensing Workflows [1.8241060496411214]
  GeoLLM-Squad introduces the novel multi-agent paradigm to remote sensing (RS)<n>Unlike existing single-agent approaches that rely on monolithic large language models (LLM), GeoLLM-Squad separates agentic orchestration from geospatial task-solving.<n>Our work enables the modular integration of diverse applications, spanning urban monitoring, forestry protection, climate analysis, and agriculture studies.
  arXiv  Detail & Related papers  (2025-01-27T17:54:31Z)
• MineAgent: Towards Remote-Sensing Mineral Exploration with Multimodal Large Language Models [8.668070469135936]
  MineAgent is a framework for multi-image reasoning and spatial-spectral integration.<n>MineBench is a benchmark for evaluating MLLMs in domain-specific mineral exploration tasks.
  arXiv  Detail & Related papers  (2024-12-23T07:08:14Z)
• 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)
• Foundation Models for Remote Sensing and Earth Observation: A Survey [101.77425018347557]
  This survey systematically reviews the emerging field of Remote Sensing Foundation Models (RSFMs)<n>It begins with an outline of their motivation and background, followed by an introduction of their foundational concepts.<n>We benchmark these models against publicly available datasets, discuss existing challenges, and propose future research directions.
  arXiv  Detail & Related papers  (2024-10-22T01:08:21Z)

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.