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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- synthetic |
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- geospatial |
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size_categories: |
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- 10K<n<100K |
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task_categories: |
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- image-segmentation |
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- image-to-3d |
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--- |
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# SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery |
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**Authors:** |
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[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> |
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<sup>1</sup> The University of Tokyo |
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<sup>2</sup> RIKEN AIP |
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**Conference:** Neural Information Processing Systems (Spotlight), 2024 |
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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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--- |
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### Overview |
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**TL;DR:** |
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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: |
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- Height estimation |
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- Land cover mapping |
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- Building change detection |
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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). |
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### Dataset Structure |
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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: |
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``` |
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${DATASET_ROOT} # Example: /home/username/project/SynRS3D/data/grid_g05_mid_v1 |
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├── opt # RGB images (.tif), also used as post-event images for building change detection |
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├── pre_opt # RGB images (.tif), used as pre-event images for building change detection |
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├── gt_nDSM # Normalized Digital Surface Model (nDSM) images (.tif) |
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├── gt_ss_mask # Land cover mapping labels (.tif) |
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├── gt_cd_mask # Building change detection masks (.tif, 0 = no change, 255 = change area) |
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└── train.txt # List of training data filenames |
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``` |
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### Class Mapping for `gt_ss_mask` |
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The land cover mapping labels (`gt_ss_mask`) are mapped to the following categories: |
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- **Bareland:** 1 |
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- **Rangeland:** 2 |
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- **Developed Space:** 3 |
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- **Road:** 4 |
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- **Trees:** 5 |
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- **Water:** 6 |
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- **Agriculture land:** 7 |
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- **Buildings:** 8 |
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### Image Breakdown by Folder |
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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: |
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- `grid` = grid-like terrain |
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- `terrain` = irregular terrain |
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- `g005`, `g05`, `g1` = GSD ranges (0.05m–0.3m, 0.3m–0.6m, and 0.6m–1m, respectively) |
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- `low`, `mid`, `high` = building height variations |
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The dataset includes the following image counts: |
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- 1,430 images – `terrain_g05_mid_v1` |
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- 10,000 images – `grid_g05_mid_v2` |
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- 2,354 images – `terrain_g05_low_v1` |
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- 3,707 images – `terrain_g05_high_v1` |
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- 880 images – `terrain_g005_mid_v1` |
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- 2,127 images – `terrain_g005_low_v1` |
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- 11,325 images – `grid_g005_mid_v2` |
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- 1,212 images – `terrain_g005_high_v1` |
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- 348 images – `terrain_g1_mid_v1` |
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- 4,285 images – `terrain_g1_low_v1` |
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- 904 images – `terrain_g1_high_v1` |
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- 3,000 images – `grid_g005_mid_v1` |
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- 2,997 images – `grid_g005_low_v1` |
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- 4,000 images – `grid_g005_high_v1` |
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- 7,000 images – `grid_g05_mid_v1` |
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- 7,098 images – `grid_g05_low_v1` |
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- 7,000 images – `grid_g05_high_v1` |
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--- |
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### Citation |
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If you find SynRS3D useful in your research, please consider citing: |
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``` |
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@article{song2024synrs3d, |
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title={SynRS3D: A Synthetic Dataset for Global 3D Semantic Understanding from Monocular Remote Sensing Imagery}, |
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author={Song, Jian and Chen, Hongruixuan and Xuan, Weihao and Xia, Junshi and Yokoya, Naoto}, |
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journal={arXiv preprint arXiv:2406.18151}, |
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year={2024} |
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} |
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``` |
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--- |
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### Contact |
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For any questions or feedback, feel free to reach out via email: **song@ms.k.u-tokyo.ac.jp**. |
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Enjoy using SynRS3D! |
