---
language:
- en
task_categories:
- image-text-to-text
tags:
- remote-sensing
- spatial-understanding
- computer-vision
- drone-imagery
---

# AirSpatial Dataset

[**Paper**](https://huggingface.co/papers/2601.01416) | [**Code**](https://github.com/VisionXLab/AirSpatialBot) | [**Model**](https://huggingface.co/erenzhou/AirSpatialBot)

AirSpatial is a spatially-aware remote sensing dataset introduced in the paper "AirSpatialBot: A Spatially-Aware Aerial Agent for Fine-Grained Vehicle Attribute Recognization and Retrieval". It specifically addresses vehicle imagery captured by drones and includes over 206K instructions.

### Key Features
- **Novel Tasks:** Introduces Spatial Grounding (SG) and Spatial Question Answering (SQA).
- **3D Annotations:** It is the first remote sensing grounding dataset to provide 3D Bounding Boxes (3DBB).
- **Fine-grained Attributes:** Focuses on vehicle attribute recognition, including brand, model, and pricing information from aerial imagery.
- **Large Scale:** Comprises over 206,000 instruction-following samples designed to enhance spatial understanding in Vision-Language Models (VLMs).

The dataset was used to train **AirSpatialBot**, an aerial agent capable of identifying specific vehicle details like brands (e.g., BYD, Tesla) and models from an aerial perspective.

## Citation

If you find this dataset or the associated work useful, please cite:

```bibtex
@ARTICLE{zhou2025airspatialbot,
  author={Zhou, Yue and Ding, Ran and Yang, Xue and Jiang, Xue and Liu, Xingzhao},
  journal={IEEE Transactions on Geoscience and Remote Sensing}, 
  title={AirSpatialBot: A Spatially-Aware Aerial Agent for Fine-Grained Vehicle Attribute Recognization and Retrieval}, 
  year={2025},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TGRS.2025.3570895}
}
```