Datasets:
language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- image-text-to-text
tags:
- math
- reasoning
- uav
- aerial-imagery
- multimodal
- vlm
Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration
Abstract
Mathematical reasoning is critical for tasks such as precise distance and area computations, trajectory estimations, and spatial analysis in unmanned aerial vehicle (UAV) based remote sensing, yet current vision-language models (VLMs) have not been adequately tested in this domain. To address this gap, we introduce AVI-Math, the first benchmark to rigorously evaluate multimodal mathematical reasoning in aerial vehicle imagery, moving beyond simple counting tasks to include domain-specific knowledge in areas such as geometry, logic, and algebra. The dataset comprises 3,773 high-quality vehicle-related questions captured from UAV views, covering 6 mathematical subjects and 20 topics. The data, collected at varying altitudes and from multiple UAV angles, reflects real-world UAV scenarios, ensuring the diversity and complexity of the constructed mathematical problems. In this paper, we benchmark 14 prominent VLMs through a comprehensive evaluation and demonstrate that, despite their success on previous multimodal benchmarks, these models struggle with the reasoning tasks in AVI-Math. Our detailed analysis highlights significant limitations in the mathematical reasoning capabilities of current VLMs and suggests avenues for future research. Furthermore, we explore the use of Chain-of-Thought prompting and fine-tuning techniques, which show promise in addressing the reasoning challenges in AVI-Math. Our findings not only expose the limitations of VLMs in mathematical reasoning but also offer valuable insights for advancing UAV-based trustworthy VLMs in real-world applications.
Latest Updates
- [2025.09.15] We released the benchmark and evaluation code.
- [2025.09.08] Accepted by ISPRS JPRS.
Contributions
Benchmark: We introduce AVI-Math, the first multimodal benchmark for mathematical reasoning in UAV imagery, covering six subjects and real-world UAV scenarios.
Analysis: We provide a comprehensive analysis, uncovering the limitations of current VLMs in mathematical reasoning and offering insights for future improvements.
Exploration: We explore the potential of Chain-of-Thought prompting and fine-tuning techniques to enhance VLM performance, providing a 215k-sample instruction set for VLMs to learn domain-specific knowledge in UAV scenarios.
Benchmark
Examples of six mathematical reasoning subjects in AVI-Math.
Please download the dataset first and then refer to the code in the evaluation to infer and evaluate the score.
Analysis
Accuracy scores on the AVI-Math. AVG: average accuracy of the six subjects. FRE: free-form question, CHO: multiple choice question, T/F: true or false question. The highest scores among models in each part and overall are highlighted in blue and red. The table exclusively employs the original model weights without fine-tuning.
Exploration
Chain-of-Thought and fine-tuning results on various VLMs.
Usage
The dataset can be downloaded from Hugging Face. For evaluation and to infer and evaluate scores using the dataset, please refer to the code provided in the official GitHub repository.
Dataset Sources
- Paper: Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration
- Repository: https://github.com/VisionXLab/avi-math
- Project Page: https://zytx121.github.io/
BibTeX:
@ARTICLE{zhou2025avimath,
author={Zhou, Yue and Feng, Litong and Lan, Mengcheng and Yang, Xue and Li, Qingyun and Ke, Yiping and Jiang, Xue and Zhang, Wayne},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
title={Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration},
year={2025},
volume={},
number={},
pages={},
doi={}
}