Update README.md

README.md

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 - en
 base_model:
 - facebook/dinov2-large
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+---
+
+# SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery
+
+**Authors:**  
+[Jian Song](https://scholar.google.ch/citations?user=CgcMFJsAAAAJ&hl=zh-CN)<sup>1,2</sup>, [Hongruixuan Chen](https://scholar.google.ch/citations?user=XOk4Cf0AAAAJ&hl=zh-CN&oi=ao)<sup>1</sup>, [Weihao Xuan](https://weihaoxuan.com/)<sup>1,2</sup>, [Junshi Xia](https://scholar.google.com/citations?user=n1aKdTkAAAAJ&hl=en)<sup>2</sup>, [Naoto Yokoya](https://scholar.google.co.jp/citations?user=DJ2KOn8AAAAJ&hl=en)<sup>1,2</sup>
+
+<sup>1</sup> The University of Tokyo  
+<sup>2</sup> RIKEN AIP
+
+**Conference:** Neural Information Processing Systems (Spotlight), 2024
+
+For more details, please refer to our [paper](https://arxiv.org/pdf/2406.18151) and visit our GitHub [repository](https://github.com/JTRNEO/SynRS3D).
+
+---
+
+### Overview
+
+**TL;DR:**  
+We are excited to release two high-performing models for **height estimation** and **land cover mapping**. These models were trained on the SynRS3D dataset using our novel domain adaptation method, **RS3DAda**. 
+
+- **Encoder:** Vision Transformer (ViT-L), pretrained with **DINOv2**  
+- **Decoder:** [DPT](https://arxiv.org/abs/2103.13413), trained from scratch
+
+These models excel in tasks involving large-scale global 3D semantic understanding from high-resolution remote sensing imagery. Feel free to integrate them into your projects for enhanced performance in related applications.
+
+---
+
+### How to Cite
+
+If you find the RS3DAda model useful in your research, please consider citing:
+
+```
+@misc{song2024synrs3dsyntheticdatasetglobal,
+      title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery}, 
+      author={Jian Song and Hongruixuan Chen and Weihao Xuan and Junshi Xia and Naoto Yokoya},
+      year={2024},
+      eprint={2406.18151},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2406.18151}, 
+}
+```
+
+---
+
+### Contact
+
+For any questions or feedback, please reach out via email at **song@ms.k.u-tokyo.ac.jp**.
+
+We hope you enjoy using the pretrained RS3DAda models!