RRS-10K: A Multitask Vision-Language Model Benchmark for Rare Remote Sensing Image Interpretation
Quick Answer
The RRS-10K benchmark introduces 10,738 rare military-related remote sensing images for evaluating vision-language models (VLMs).
Quick Take
Current show only moderate zero-shot performance, revealing weaknesses in visual grounding and complex reasoning tasks, thus highlighting the need for improved models in rare scene interpretation.
Key Points
- RRS-10K consists of 10,738 military-related remote sensing images.
- Benchmark includes multiple format question-answer pairs for evaluation.
- Current VLMs show moderate zero-shot performance in rare scene interpretation.
- Weaknesses identified in visual grounding and complex semantic reasoning.
- RRS-10K aids in analyzing failure modes in long-tail remote sensing tasks.
DeepSignal Analysis
What happened
The RRS-10K benchmark has been introduced to evaluate vision-language models (VLMs) using 10,738 military-related remote sensing images. Current VLMs demonstrate moderate zero-shot performance, indicating limitations in visual grounding and complex reasoning tasks.
Key evidence
- RRS-10K consists of 10,738 military-related remote sensing images and multiple question-answer pairs for evaluation.
- Current VLMs show only moderate zero-shot performance on rare remote sensing image interpretation, particularly in visual grounding and complex reasoning.
- The benchmark organizes images into three capability dimensions, six sub-dimensions, and 20 leaf tasks, focusing on perception, reasoning, and robustness.
Why it matters
Understanding the limitations of VLMs in interpreting rare remote sensing images is crucial for advancing AI capabilities in this domain. The introduction of RRS-10K highlights the need for improved models that can handle complex scenarios, which is essential for military and security applications.
What to watch
Paper Resources
📖 Reader Mode
~2 min readAbstract:Vision-language models (VLMs) have achieved strong performance on general remote sensing tasks. However, their capability for rare scenes remains insufficiently understood, because existing benchmarks are dominated by common urban and rural imagery. To address this gap, we present RRS-10K, a benchmark for rare remote sensing image interpretation. RRS-10K contains 10,738 military-related remote sensing images and corresponding multiple format question-answer pairs for comprehensive evaluation. All of the images are collected from first-hand sources and organized into three capability dimensions, six sub-dimensions, and 20 leaf tasks, covering perception, reasoning, and robustness. To improve the quality of multiple-choice questions, we introduce a similarity-based distractor filtering strategy (SDFS) during benchmark construction. We further evaluate 52 representative models and show that current VLMs achieve only moderate zero-shot performance on rare remote sensing image interpretation, with clear weaknesses in visual grounding, referring segmentation, and complex semantic reasoning tasks. RRS-10K enables systematic analysis of failure modes in long-tail remote sensing interpretation and provides guidance for developing more reliable remote sensing VLMs.
| Subjects: | Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV) |
| Cite as: | arXiv:2607.24810 [cs.AI] |
| (or arXiv:2607.24810v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2607.24810 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yuqiao Lai [view email]
[v1]
Mon, 13 Jul 2026 08:02:57 UTC (3,238 KB)
— Originally published at arxiv.org
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