Improve dataset card: Add abstract, detailed description, usage, update task categories and paper link

This PR significantly enhances the dataset card for AVI-Math by incorporating rich content from the project's GitHub README.

Key updates include:
- A detailed introduction, abstract, and contributions section.
- Visual examples of the benchmark, analysis, and exploration sections with absolute image URLs.
- A "Usage" section directing users to the GitHub repository for evaluation code.
- Addition of `image-text-to-text` to the `task_categories` metadata, alongside `question-answering`, to better reflect the multimodal nature of the dataset.
- Addition of relevant tags: `uav`, `aerial-imagery`, `multimodal`, `vlm`.
- Updating the paper link to the Hugging Face paper page (`https://huggingface.co/papers/2509.10059`).
- Adding the project page link (`https://zytx121.github.io/`) to `Dataset Sources`.

README.md

@@ -1,23 +1,101 @@
 ---
+language:
+- en
 license: apache-2.0
+size_categories:
+- 1K<n<10K
 task_categories:
 - question-answering
-language:
-- en
+- image-text-to-text
 tags:
 - math
 - reasoning
-size_categories:
-- 1K<n<10K
+- uav
+- aerial-imagery
+- multimodal
+- vlm
 ---
 
+# Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration
 
-## Dataset Sources
+<p align="center">
+    <img src="https://github.com/VisionXLab/avi-math/blob/main/images/avi-math.png?raw=true" width=100%>
+</p>
+
+## 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.
+
+<p align="center">
+  <img src="https://github.com/VisionXLab/avi-math/blob/main/images/cat.png?raw=true" width=50%>
+  <div style="display: inline-block; color: #999; padding: 2px;">
+      ARI: arithmetic, CNT: counting, ALG: algebra, STA: statistics, LOG: logic, GEO: geometry.
+  </div>
+</p>
+
+---
+
+## 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.
 
-<!-- Provide the basic links for the model. -->
+---
+
+## Benchmark
+
+Examples of six mathematical reasoning subjects in AVI-Math.
+
+<p align="center">
+  <img src="https://github.com/VisionXLab/avi-math/blob/main/images/bench1.png?raw=true" width=100%>
+</p>
+<p align="center">
+  <img src="https://github.com/VisionXLab/avi-math/blob/main/images/bench2.png?raw=true" width=100%>
+</p>
+
+Please download the [dataset](https://huggingface.co/datasets/erenzhou/AVI-Math) 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.
+
+<p align="center">
+  <img src="https://github.com/VisionXLab/avi-math/blob/main/images/analysis.png?raw=true" width=100%>
+</p>
+
+---
+
+## Exploration
+
+Chain-of-Thought and fine-tuning results on various VLMs.
+
+<p align="center">
+  <img src="https://github.com/VisionXLab/avi-math/blob/main/images/explore.png?raw=true" width=100%>
+</p>
+
+---
+
+## 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](https://github.com/VisionXLab/avi-math).
+
+## Dataset Sources
 
-- **Repository:** https://github.com/VisionXLab/avi-math
-- **Paper:** https://arxiv.org/abs/2509.10059
+-   **Paper:** [Multimodal Mathematical Reasoning Embedded in Aerial Vehicle Imagery: Benchmarking, Analysis, and Exploration](https://huggingface.co/papers/2509.10059)
+-   **Repository:** https://github.com/VisionXLab/avi-math
+-   **Project Page:** https://zytx121.github.io/
 
 **BibTeX:**