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.
Towards vision-language geo-foundation model: A survey.arXiv preprint arXiv:2406.09385, 2024a
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RemoteZero replaces coordinate supervision with intrinsic semantic verification to enable box-free GRPO training and self-evolution for geospatial reasoning.
RemoteShield improves robustness of Earth observation MLLMs by training on semantic equivalence clusters of clean and perturbed inputs via preference learning to maintain consistent reasoning under noise.
Geo-R1 uses reasoning-centric reinforcement fine-tuning to improve few-shot performance and generalization in geospatial referring expression understanding over supervised baselines.
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets
citing papers explorer
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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.
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RemoteZero: Geospatial Reasoning with Zero Human Annotations
RemoteZero replaces coordinate supervision with intrinsic semantic verification to enable box-free GRPO training and self-evolution for geospatial reasoning.
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RemoteShield: Enable Robust Multimodal Large Language Models for Earth Observation
RemoteShield improves robustness of Earth observation MLLMs by training on semantic equivalence clusters of clean and perturbed inputs via preference learning to maintain consistent reasoning under noise.
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Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning
Geo-R1 uses reasoning-centric reinforcement fine-tuning to improve few-shot performance and generalization in geospatial referring expression understanding over supervised baselines.
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Earth Science Foundation Models: From Perception to Reasoning and Discovery
The paper delivers a unified review and roadmap of Earth science foundation models, structured by capability depth from perception to agentic reasoning and by application breadth across atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, while compiling over 200 datasets