Update README.md
Browse files# 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).
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### 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.
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### 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!