Improve dataset card: add task category, GitHub link, and directory structure

Hi! I'm Niels from the Hugging Face community science team.

I've updated the dataset card to include:
- The `image-to-3d` task category in the metadata.
- A link to the official GitHub repository.
- Detailed information about the dataset directory structure (JAX and NYC) to help users understand how to organize the files for training.
- A citation section with the appropriate BibTeX entry.

README.md

@@ -1,11 +1,59 @@
 ---
 license: apache-2.0
+task_categories:
+- image-to-3d
 ---
 
 # The datasets for Skyfall-GS
 
-> Skyfall-GS: Synthesizing Immersive 3D Urban Scenes from Satellite Imagery
+[Project Page](https://skyfall-gs.jayinnn.dev/) | [Paper](https://arxiv.org/abs/2510.15869) | [GitHub](https://github.com/jayin92/skyfall-gs)
 
-Check our project page: https://skyfall-gs.jayinnn.dev/
+Skyfall-GS is a hybrid framework that synthesizes immersive, city-block scale 3D urban scenes by combining satellite reconstruction with diffusion refinement. This repository contains the JAX and NYC datasets used for training and evaluation.
 
-arXiv link: arxiv.org/abs/2510.15869
+## Dataset Structure
+
+According to the [official GitHub documentation](https://github.com/jayin92/skyfall-gs), the datasets should be organized in the `data/` directory as follows:
+
+```
+data/
+├── datasets_JAX/
+│   ├── JAX_004
+│   ├── JAX_068
+│   └── ...
+└── datasets_NYC/
+    ├── NYC_004
+    ├── NYC_010
+    └── ...
+```
+
+Each individual scene directory (e.g., `JAX_068`) contains the following structure:
+
+```
+your_dataset/
+├── images/                    # RGB images
+│   ├── image_001.png
+│   ├── image_002.png
+│   └── ...
+├── masks/                   # Binary masks for valid pixels (optional)
+│   ├── *.npy               # NumPy format
+│   ├── *.png               # PNG format
+│   └── ...
+├── transforms_train.json      # Training camera parameters
+├── transforms_test.json       # Testing camera parameters (optional)
+└── points3D.txt              # 3D point cloud
+```
+
+## Citation
+
+If you find this work or the datasets useful, please consider citing:
+
+```bibtex
+@article{lee2025SkyfallGS,
+  title = {{Skyfall-GS}: Synthesizing Immersive {3D} Urban Scenes from Satellite Imagery},
+  author = {Jie-Ying Lee and Yi-Ruei Liu and Shr-Ruei Tsai and Wei-Cheng Chang and Chung-Ho Wu and Jiewen Chan and Zhenjun Zhao and Chieh Hubert Lin and Yu-Lun Liu},
+  journal = {arXiv preprint},
+  year = {2025},
+  eprint = {2510.15869},
+  archivePrefix = {arXiv}
+}
+```