---
license: cc-by-nc-4.0
tags:
- synthetic
- geospatial
size_categories:
- 10K<n<100K
task_categories:
- image-segmentation
- image-to-3d
---

# 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:**  
SynRS3D is a comprehensive synthetic remote sensing dataset designed to improve global 3D semantic understanding from monocular high-resolution imagery. It includes data for three key tasks:
- Height estimation
- Land cover mapping
- Building change detection

Additionally, we introduce RS3DAda, a novel multi-task domain adaptation method to enhance performance across these tasks. Learn more about RS3DAda in our [repository](https://github.com/JTRNEO/SynRS3D).

### Dataset Structure

The dataset consists of 17 folders and includes a total of **69,667 images** at a resolution of 512x512. After downloading and extracting the files, ensure the directory structure follows this format:

```
${DATASET_ROOT}  # Example: /home/username/project/SynRS3D/data/grid_g05_mid_v1
├── opt           # RGB images (.tif), also used as post-event images for building change detection
├── pre_opt       # RGB images (.tif), used as pre-event images for building change detection
├── gt_nDSM       # Normalized Digital Surface Model (nDSM) images (.tif)
├── gt_ss_mask    # Land cover mapping labels (.tif)
├── gt_cd_mask    # Building change detection masks (.tif, 0 = no change, 255 = change area)
└── train.txt     # List of training data filenames
```

### Class Mapping for `gt_ss_mask`

The land cover mapping labels (`gt_ss_mask`) are mapped to the following categories:

- **Bareland:** 1
- **Rangeland:** 2
- **Developed Space:** 3
- **Road:** 4
- **Trees:** 5
- **Water:** 6
- **Agriculture land:** 7
- **Buildings:** 8

### Image Breakdown by Folder

The dataset is organized into grid-like and irregular terrain. It includes a range of ground sampling distances (GSDs) and variations in building heights. The folder naming convention indicates these characteristics:  
- `grid` = grid-like terrain  
- `terrain` = irregular terrain  
- `g005`, `g05`, `g1` = GSD ranges (0.05m–0.3m, 0.3m–0.6m, and 0.6m–1m, respectively)  
- `low`, `mid`, `high` = building height variations

The dataset includes the following image counts:

- 1,430 images – `terrain_g05_mid_v1`
- 10,000 images – `grid_g05_mid_v2`
- 2,354 images – `terrain_g05_low_v1`
- 3,707 images – `terrain_g05_high_v1`
- 880 images – `terrain_g005_mid_v1`
- 2,127 images – `terrain_g005_low_v1`
- 11,325 images – `grid_g005_mid_v2`
- 1,212 images – `terrain_g005_high_v1`
- 348 images – `terrain_g1_mid_v1`
- 4,285 images – `terrain_g1_low_v1`
- 904 images – `terrain_g1_high_v1`
- 3,000 images – `grid_g005_mid_v1`
- 2,997 images – `grid_g005_low_v1`
- 4,000 images – `grid_g005_high_v1`
- 7,000 images – `grid_g05_mid_v1`
- 7,098 images – `grid_g05_low_v1`
- 7,000 images – `grid_g05_high_v1`

---

### Citation

If you find SynRS3D useful in your research, please consider citing:

```
@article{song2024synrs3d,
title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery},
author={Song, Jian and Chen, Hongruixuan and Xuan, Weihao and Xia, Junshi and Yokoya, Naoto},
journal={arXiv preprint arXiv:2406.18151},
year={2024}
}
```

---

### Contact

For any questions or feedback, feel free to reach out via email: **song@ms.k.u-tokyo.ac.jp**.

Enjoy using SynRS3D!