SenseBench is the first physics-based benchmark with 10K+ instances and dual protocols to evaluate VLMs on remote sensing low-level perception and diagnostic description, revealing domain bias and specific failure modes.
Mixed citations
arXiv preprint arXiv:2503.11070 (2025)
Mixed citation behavior. Most common role is background (40%).
10
Pith papers citing it
Background
40% of classified citations
citation-role summary
background 2
baseline 2
method 1
citation-polarity summary
years
2026 10verdicts
UNVERDICTED 10representative citing papers
Unfine-tuned MLLMs outperform fine-tuned models on remote sensing image captioning when captions are scored by their ability to reconstruct the source image, and a training-free self-correction method achieves SOTA performance.
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.
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.
RSICCLLM introduces a post-training framework with RSICI dataset, difference-aware supervised fine-tuning, and dual-negative preference optimization that claims to outperform much larger models on remote sensing image change captioning.
DiffuSAM fuses diffusion-based localization cues with SAM models to deliver over 14% higher Acc@0.5 in zero-shot object grounding for remote sensing imagery compared to prior methods.
Introduces SGR and CGR refinement pipelines plus majority-voting ensemble to improve visual grounding accuracy in remote sensing by combining RemoteSAM and SAM3.
A text-guided framework for remote sensing image transmission uses low-res images and compact text to reduce data volume to 2%, with text-conditioned reconstruction achieving PSNRs of 16.36-27.41 dB on tested datasets.
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.
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.
citing papers explorer
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SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models
SenseBench is the first physics-based benchmark with 10K+ instances and dual protocols to evaluate VLMs on remote sensing low-level perception and diagnostic description, revealing domain bias and specific failure modes.
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Evaluating Remote Sensing Image Captions Beyond Metric Biases
Unfine-tuned MLLMs outperform fine-tuned models on remote sensing image captioning when captions are scored by their ability to reconstruct the source image, and a training-free self-correction method achieves SOTA performance.
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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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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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RSICCLLM: A Multimodal Large Language Model for Remote Sensing Image Change Captioning
RSICCLLM introduces a post-training framework with RSICI dataset, difference-aware supervised fine-tuning, and dual-negative preference optimization that claims to outperform much larger models on remote sensing image change captioning.
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DiffuSAM: Diffusion Guided Zero-Shot Object Grounding for Remote Sensing Imagery
DiffuSAM fuses diffusion-based localization cues with SAM models to deliver over 14% higher Acc@0.5 in zero-shot object grounding for remote sensing imagery compared to prior methods.
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Improving Visual Grounding in Remote Sensing via Cluster-Guided Refinement and Model Ensemble Voting
Introduces SGR and CGR refinement pipelines plus majority-voting ensemble to improve visual grounding accuracy in remote sensing by combining RemoteSAM and SAM3.
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Text-RSIR: A Text-Guided Framework for Efficient Remote Sensing Image Transmission and Reconstruction
A text-guided framework for remote sensing image transmission uses low-res images and compact text to reduce data volume to 2%, with text-conditioned reconstruction achieving PSNRs of 16.36-27.41 dB on tested datasets.
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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.
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Vision-and-Language Navigation for UAVs: Progress, Challenges, and a Research Roadmap
A survey of UAV vision-and-language navigation that establishes a methodological taxonomy, reviews resources and challenges, and proposes a forward-looking research roadmap.