XHRBench

pretty_name: XHRBench
license: apache-2.0
task_categories:
  - image-text-to-text
language:
  - en
tags:
  - remote-sensing
  - mllm
  - vqa
  - image-captioning
  - high-resolution
  - multimodal
  - ultra-high-resolution
  - uav
  - benchmark

XHRBench

Ultra-High-Resolution Remote Sensing Understanding and Reasoning

🤗 Hugging Face · 🤖 ModelScope · 📄 Paper · 💻 Code

English | 中文

📚 Introduction

XHRBench evaluates fine-grained perception and complex reasoning in multimodal large language models using ultra-high-resolution remote-sensing imagery. This repository retains the name XHRBench and belongs to the same RSHR benchmark project as RSHR-Bench, with a different file layout.

The full benchmark described in the paper contains 5,329 full-scene images, with a long side of at least 4,000 pixels and up to approximately 3 × 10⁸ pixels (300 MP) per image. This repository's dataset.json contains 1,932 VQA examples; the package's example count is distinct from the full benchmark's image count.

🎯 Dataset at a Glance

Item	Details
Current JSON release	1,932 VQA examples
Task categories	17 original category identifiers
Images per example	1 image: 1,862; 2 images: 50; 3 images: 20
Data language	English
Main files	`dataset.json`, `images/`, `data/train-00000-of-00001.parquet`
Full benchmark scope	5,329 full-scene images; 9 perception categories and 4 reasoning types

The benchmark draws on DOTA-v2.0, MiniFrance, FAIR1M, HRSCD, XLRS-Bench, and the team's 100 MP UAV imagery. Its two-stage Human–LLM Adversarial Verification pipeline combines LLM adversarial filtering with human review to reduce questions answerable from language priors alone.

🌟 Task Design

Task family	Evaluation focus
Multiple-Choice VQA (MCQ)	Visual decisions within a fixed answer space, including single-turn and multi-turn dialogues
Open-Ended VQA (OEQ)	Free-form visual understanding and compositional reasoning
Image Captioning (IC)	Global scene summaries and regional descriptions
Single-Image Evaluation (SIE)	Multi-scale perception and reasoning within an ultra-high-resolution image

These four families describe the full evaluation design in the paper. The dataset.json in this repository contains VQA annotations with answer options. See the paper and code for the complete evaluation protocol.

📦 Data Format

dataset.json                       # List of VQA annotations
images/                            # Original images referenced by annotations
data/train-00000-of-00001.parquet  # Repository-provided Parquet file
assets/                            # Dataset-card figures and logos

Field	Description
`id`	Sample identifier
`images`	List of relative image paths
`question`	Question text
`options`	Mapping from option letters to option text
`answer`	Gold answer option
`category`	Original task category identifier

Image paths are relative to the repository root. Category identifiers retain their original spelling. The train component in the Parquet filename is a packaging label; it does not by itself define the paper's training/test protocol.

Category distribution

Category (original identifier)	Examples
`anomaly_detection`	50
`color`	200
`detection`	100
`future_prediction_two_image`	50
`multi_region_join_contrast`	20
`multi_region_join_contrast_singal`	50
`multitrun-future_prediction`	150
`multiturn_object_state_judgement`	132
`multturn_anomaly_detection`	60
`object_clasfication`	200
`object_counting`	200
`object_grounding`	200
`object_state_judgement`	50
`regional_grouding`	70
`regionla_counting`	100
`relation`	200
`shape`	100
Total	1,932

🚀 Quick Start

Inspect the annotations first, then download the images you need.

import json
from huggingface_hub import hf_hub_download

annotation_path = hf_hub_download(
    repo_id="RL-MIND/XHRBench",
    repo_type="dataset",
    filename="dataset.json",
)
with open(annotation_path, encoding="utf-8") as f:
    samples = json.load(f)

sample = samples[0]
print(sample["category"], sample["question"], sample["options"])

# Download only the image needed for this sample.
image_path = hf_hub_download(
    repo_id="RL-MIND/XHRBench",
    repo_type="dataset",
    filename=sample["images"][0],
)

📊 Evaluation and Visualizations

The original project reports evaluations of 14 models, including GPT-4o, Gemini 1.5 Pro, Qwen2.5-VL, and remote-sensing models such as GeoChat and VHM, across multiple-choice VQA, open-ended VQA, and image captioning. The original overview, leaderboard, and task-distribution figures are retained below; consult the paper for model versions and evaluation settings.

Evaluation figures

Task distribution

💡 Usage Notes

Intended for evaluating visual understanding, perception, and reasoning in ultra-high-resolution remote sensing.
Original high-resolution images can require substantial disk space and decoding memory; download them selectively when possible.
Use dataset.json together with images/ and preserve the relative paths.
This repository and RSHR-Bench use different packages. File and shard counts should not be used as direct measures of benchmark size.

📜 License and Citation

The repository declares the Apache-2.0 license. The imagery draws on other public remote-sensing datasets; consult the original licenses and terms when using those source datasets.

This dataset is associated with A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs. Please cite the paper when using the benchmark:

@article{dang2025RSHR,
  title={A Benchmark for Ultra-High-Resolution Remote Sensing MLLMs},
  author={Dang, Yunkai and Zhu, Meiyi and Wang, Donghao and Zhang, Yizhuo and Yang, Jiacheng and Fan, Qi and Yang, Yuekun and Li, Wenbin and Miao, Feng and Gao, Yang},
  journal={arXiv preprint arXiv:2512.17319},
  year={2025}
}