RS-Claw enables remote sensing agents to actively explore tools via hierarchical skill trees, achieving up to 86% token compression and outperforming flat registration and RAG baselines on Earth-Bench.
RS-Agent: Au- tomating remote sensing tasks through intelligent agent
11 Pith papers cite this work. Polarity classification is still indexing.
11
Pith papers citing it
citation-role summary
background 3
dataset 1
citation-polarity summary
years
2026 11verdicts
UNVERDICTED 11representative citing papers
DORA is the first end-to-end agentic benchmark for LLM-based disaster response, covering perception, spatial analysis, evacuation planning, temporal reasoning, and report generation over heterogeneous geospatial data, with evaluations of 13 frontier models revealing tool-use and composition failures
RemoteAgent uses RL fine-tuning on VagueEO to align MLLMs for vague EO intent recognition, handling simple tasks internally and routing dense predictions to tools via Model Context Protocol.
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 limitations in grounding and safety.
SAGA is a schema-grounded agent framework that extracts facts, validates schemas, plans augmentation strategies, and evaluates generated SAR samples for quality and downstream utility.
A bidirectional semantic complementary tool retrieval method using planning-based query enhancement and dynamic tool dependency graphs with neighborhood aggregation improves retrieval accuracy on remote sensing and general tool tasks.
MONETA is the first multimodal benchmark for industry classification using text and geographic sources, with MLLM baselines at 62-74% accuracy and up to 22.8% gains from multi-turn context enrichment and explanations.
JL1-CC&QA extends JL1-CD with change captioning and QA annotations on 5,000 bi-temporal Jilin-1 satellite image pairs to support multi-task semantic change understanding.
GeoDisaster provides a new benchmark for operational disaster geo-intelligence and proposes an RCEA-trained multi-agent framework with 18 geospatial tools that improves tool use and decision consistency over existing RS-VLMs.
The LMMP framework improves tool-calling accuracy and task success rates for Earth observation agents by grounding plans in multimodal features and remote sensing expert knowledge via a two-stage training process.
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.
citing papers explorer
-
RS-Claw: Progressive Active Tool Exploration via Hierarchical Skill Trees for Remote Sensing Agents
RS-Claw enables remote sensing agents to actively explore tools via hierarchical skill trees, achieving up to 86% token compression and outperforming flat registration and RAG baselines on Earth-Bench.
-
Can LLM Agents Respond to Disasters? Benchmarking Heterogeneous Geospatial Reasoning in Emergency Operations
DORA is the first end-to-end agentic benchmark for LLM-based disaster response, covering perception, spatial analysis, evacuation planning, temporal reasoning, and report generation over heterogeneous geospatial data, with evaluations of 13 frontier models revealing tool-use and composition failures
-
RemoteAgent: Bridging Vague Human Intents and Earth Observation with RL-based Agentic MLLMs
RemoteAgent uses RL fine-tuning on VagueEO to align MLLMs for vague EO intent recognition, handling simple tasks internally and routing dense predictions to tools via Model Context Protocol.
-
Agentic AI in Remote Sensing: Foundations, Taxonomy, and Emerging Systems
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 limitations in grounding and safety.
-
A Task-Driven and Quality-Assured Agent Framework for SAR Data Generation
SAGA is a schema-grounded agent framework that extracts facts, validates schemas, plans augmentation strategies, and evaluates generated SAR samples for quality and downstream utility.
-
Bidirectional Semantic Complementary Tool Retrieval for Remote Sensing Agents
A bidirectional semantic complementary tool retrieval method using planning-based query enhancement and dynamic tool dependency graphs with neighborhood aggregation improves retrieval accuracy on remote sensing and general tool tasks.
-
MONETA: Multimodal Industry Classification through Geographic Information with Multi Agent Systems
MONETA is the first multimodal benchmark for industry classification using text and geographic sources, with MLLM baselines at 62-74% accuracy and up to 22.8% gains from multi-turn context enrichment and explanations.
-
JL1-CC&QA: Extending the JL1-CD Benchmark with Change Captioning and Question Answering
JL1-CC&QA extends JL1-CD with change captioning and QA annotations on 5,000 bi-temporal Jilin-1 satellite image pairs to support multi-task semantic change understanding.
-
GeoDisaster: Benchmarking Orchestrated Agents for Operational Disaster Geo-Intelligence
GeoDisaster provides a new benchmark for operational disaster geo-intelligence and proposes an RCEA-trained multi-agent framework with 18 geospatial tools that improves tool use and decision consistency over existing RS-VLMs.
-
Bridging Perception and Action: A Lightweight Multimodal Meta-Planner Framework for Robust Earth Observation Agents
The LMMP framework improves tool-calling accuracy and task success rates for Earth observation agents by grounding plans in multimodal features and remote sensing expert knowledge via a two-stage training process.
-
Agentic AI for Remote Sensing: Technical Challenges and Research Directions
Position paper identifies structural challenges in applying generic agentic AI to Earth Observation and outlines design principles for EO-native agents focused on geospatial state and validity.