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.
Choice: Benchmarking the remote sensing capabilities of large vision-language models
4 Pith papers cite this work. Polarity classification is still indexing.
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2026 4verdicts
UNVERDICTED 4representative citing papers
Current MLLMs show weak performance on small object understanding tasks, but fine-tuning with the new SOU-Train dataset measurably improves their capabilities.
GeoAgentBench supplies a live execution environment and Plan-and-React architecture that lets tool-using AI agents handle multi-step GIS tasks more robustly than prior static evaluation methods.
A geometry-faithful RPC-consistent protocol for satellite multi-view features shows semantic similarity does not guarantee geometric matchability and that 2D backbones remain competitive with 3D-aware models under proper constraints.
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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Can Multimodal Large Language Models Truly Understand Small Objects?
Current MLLMs show weak performance on small object understanding tasks, but fine-tuning with the new SOU-Train dataset measurably improves their capabilities.
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GeoAgentBench: A Dynamic Execution Benchmark for Tool-Augmented Agents in Spatial Analysis
GeoAgentBench supplies a live execution environment and Plan-and-React architecture that lets tool-using AI agents handle multi-step GIS tasks more robustly than prior static evaluation methods.
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Geometric Consistency Protocol for Foundation Model Features in Multi-View Satellite Imagery
A geometry-faithful RPC-consistent protocol for satellite multi-view features shows semantic similarity does not guarantee geometric matchability and that 2D backbones remain competitive with 3D-aware models under proper constraints.