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--- |
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language: |
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- en |
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license: apache-2.0 |
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task_categories: |
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- image-text-to-text |
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tags: |
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- remote-sensing |
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- mllm |
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- vqa |
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- image-captioning |
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- high-resolution |
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- uav |
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--- |
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# *RSHR*: A Benchmark for MLLMs on Ultra-High-Resolution Remote Sensing Data |
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Paper: [A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs](https://huggingface.co/papers/2512.17319) |
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Code: [https://github.com/Yunkaidang/RSHR](https://github.com/Yunkaidang/RSHR) |
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``` |
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If our project helps you, please give us a star ⭐ on GitHub to support us💕 |
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``` |
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## 🔥 News |
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- **`2025-11-14`** 🎉 We released the paper : *RSHR*: A Benchmark for MLLMs on Ultra-High-Resolution Remote Sensing Data. |
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- **`2025-11-14`** 🎉 We released the benchmark dataset on Hugging Face: [XHRBench (FelixKAI/XHRBench)](https://huggingface.co/datasets/FelixKAI/XHRBench). |
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## 😼*RSHR* Overview |
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- **Large-scale ultra-high-resolution benchmark:** RSHR is designed to evaluate fine-grained perception and complex reasoning of multimodal large language models in remote sensing, comprising **5,329 full-scene images** with native resolutions from **4K up to 3 × 10^8 pixels (300 MP)**. |
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- **Diverse expert-annotated data sources:** The dataset aggregates expert-annotated data from **DOTA-v2.0, MiniFrance, FAIRIM, HRSCD, XLRS-Bench**, and our own **100MP UAV-captured imagery**, covering a wide variety of real-world remote sensing scenarios. |
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- **Comprehensive tasks and rigorous evaluation pipeline:** RSHR spans **9 perception categories** and **4 reasoning types**, supporting both single-image and multi-image/multi-turn dialogues, and adopts a two-stage **Human–LLM Adversarial Verification** pipeline (LLM adversarial filtering + human review) to eliminate questions solvable by language priors alone, ensuring that models must truly see the image to answer. |
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### **🧠 Comprehensive Task Suite** |
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We categorize the evaluation into **four main task families** to support diverse usage scenarios, covering **9 perception categories** (e.g., Color, Orientation, Regional Grounding) and **4 reasoning types**. |
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- 🧩 **Multiple-Choice VQA (MCQ)**: Evaluates decision-making within a fixed answer space, covering both single-turn and multi-turn dialogues. |
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- ✍️ **Open-Ended VQA (OEQ)**: Assesses free-form visual understanding and compositionality without the reliance on option priors, offering a more accurate measure of MLLM capabilities. |
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- 📝 **Image Captioning (IC)**: Requires concise, accurate descriptions for both **Global** scenes (whole-image summary) and **Regional** details (directional sectors)。 |
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- 🔍 **Single-Image Evaluation (SIE)**: A specialized protocol to test deep understanding of ultra-high-resolution images (4K to $3 \times 10^8$ pixels), probing multi-scale perception and reasoning on a per-image basis. |
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## 🔖Evaluation Results |
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We evaluated **14 state-of-the-art models**, including general-purpose MLLMs (e.g., GPT-4o, Gemini 1.5 Pro, Qwen2.5-VL) and remote-sensing specialist models (e.g., GeoChat, VHM). The evaluation covers **Multiple-Choice VQA**, **Open-Ended VQA**, and **Image Captioning**. |
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### 📊 1. Main Leaderboard (Multiple-Choice) |
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Closed-source models dominate the leaderboard, yet they still struggle with complex reasoning tasks requiring fine-grained visual evidence. |
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### 📉 2. Performance Analysis: Perception vs. Reasoning |
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We further analyze the correlation between perception and reasoning capabilities using Open-Ended VQA evaluation to avoid random guessing. |
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### 📏 3. Impact of Resolution (Key Insight) |
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Does higher resolution support lead to better performance? Our Single-Image Evaluation reveals a critical robustness issue. |
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<table> |
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<tr> |
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<td align="center"> |
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<img src="assets/image_201.png" width="600"> |
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</td> |
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<td align="center"> |
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<img src="assets/image_204.png" width="400"> |
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</td> |
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<td align="center"> |
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<img src="assets/image_205.png" width="400"> |
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</td> |
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</tr> |
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</table> |
