FelixKAI/XHRBench

RSHR: A Benchmark for MLLMs on Ultra-High-Resolution Remote Sensing Data

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🔥 News

• 2025-11-14 🎉 We released the paper : RSHR: A Benchmark for MLLMs on Ultra-High-Resolution Remote Sensing Data.

😼RSHR Overview

• 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).

• 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.

• 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.

🧠 Comprehensive Task Suite

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.

• 🧩 Multiple-Choice VQA (MCQ): Evaluates decision-making within a fixed answer space, covering both single-turn and multi-turn dialogues.
• ✍️ 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.
• 📝 Image Captioning (IC): Requires concise, accurate descriptions for both Global scenes (whole-image summary) and Regional details (directional sectors)。
• 🔍 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.

🔖Evaluation Results

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.

📊 1. Main Leaderboard (Multiple-Choice)

Closed-source models dominate the leaderboard, yet they still struggle with complex reasoning tasks requiring fine-grained visual evidence.

📉 2. Performance Analysis: Perception vs. Reasoning

We further analyze the correlation between perception and reasoning capabilities using Open-Ended VQA evaluation to avoid random guessing.

📏 3. Impact of Resolution (Key Insight)

Does higher resolution support lead to better performance? Our Single-Image Evaluation reveals a critical robustness issue.