GeoMeld provides a large-scale aligned multimodal remote sensing dataset with verified semantic captions and a joint pretraining method that improves downstream transfer and cross-sensor robustness in foundation models.
Ringmo-agent: A unified re- mote sensing foundation model for multi-platform and multi- modal reasoning
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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.
An audit of 152 papers reveals that geospatial foundation models lack standardized evaluations, training controls, and weight releases, so no one knows the state of the art.
MPerS dynamically mixes semantic guidance from MLLM-generated RS captions with DINOv3 features via MixExperts and Linguistic Query Guided Attention to achieve superior semantic segmentation on three public remote sensing datasets.
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.
citing papers explorer
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GeoMeld: Toward Semantically Grounded Foundation Models for Remote Sensing
GeoMeld provides a large-scale aligned multimodal remote sensing dataset with verified semantic captions and a joint pretraining method that improves downstream transfer and cross-sensor robustness in foundation models.
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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.
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No One Knows the State of the Art in Geospatial Foundation Models
An audit of 152 papers reveals that geospatial foundation models lack standardized evaluations, training controls, and weight releases, so no one knows the state of the art.
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MPerS: Dynamic MLLM MixExperts Perception-Guided Remote Sensing Scene Segmentation
MPerS dynamically mixes semantic guidance from MLLM-generated RS captions with DINOv3 features via MixExperts and Linguistic Query Guided Attention to achieve superior semantic segmentation on three public remote sensing datasets.
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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.