Add paper link, task categories and improve dataset description

README.md

@@ -1,6 +1,42 @@
 ---
 language:
 - en
+task_categories:
+- image-text-to-text
+tags:
+- remote-sensing
+- spatial-understanding
+- computer-vision
+- drone-imagery
 ---
 
-https://github.com/VisionXLab/AirSpatialBot
+# 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}
+}
+```