Awesome
Hi-UCD Series Dataset
Hi-UCD (ultra-High Urban Change Detection) series datasets are designed for deep learning based urban semantic change detection.
News
- 2023/9/10 Hi-UCD mini is released.
- 2024/7/18 Hi-UCD is released.
Hi-UCD mini
The Hi-UCD mini dataset can be downloaded through the Google Drive link. Please submit a download request through the link and I will process your request as soon as possible.
Hi-UCD
The characteristics of Hi-UCD:
- 40800 pairs ultra-high resolution images (0.1 m) for Tallinn, Estonia.
- Focus on refined urban semantic change detection.
- Include 2 years of images, 9 land cover classes and 48 semantic change classes.
- Tasks that can be performed on this dataset: semantic segmentation, binary change detection, semantic change detection, etc.
The Hi-UCD dataset can be downloaded through the Baidu Drive link and OneDrive link.
If you want to get the test scores, please join our hosted benchmark platform: semantic change detection.
Semantic label and palette
| Number | Class | Palette |
|---|---|---|
| 0 | unlabeled | (255,255,255) |
| 1 | Water | (0, 153,255 ) |
| 2 | grass | (202, 255, 122) |
| 3 | building | (230, 0, 0) |
| 4 | green house | (230, 0, 255) |
| 5 | road | (255, 230, 0) |
| 6 | bridge | (255 ,181 ,197) |
| 7 | others | (0, 255, 230) |
| 8 | bare land | (175, 122, 255) |
| 9 | woodland | (26,255,0) |
Change label and palette
| Number | Class | Palette |
|---|---|---|
| 0 | unlabeled | (255,255,255) |
| 1 | change | (0,0,0) |
| 2 | unchanged | (220, 0, 0) |
Citation
If you use Hi-UCD series dataset in your research, please cite our papers as follows:
@article{tian2020hi,
title={Hi-UCD: A large-scale dataset for urban semantic change detection in remote sensing imagery},
author={Tian, Shiqi and Ma, Ailong and Zheng, Zhuo and Zhong, Yanfei},
journal={arXiv preprint arXiv:2011.03247},
year={2020}
}
@article{tian2022large,
title={Large-scale deep learning based binary and semantic change detection in ultra high resolution remote sensing imagery: From benchmark datasets to urban application},
author={Tian, Shiqi and Zhong, Yanfei and Zheng, Zhuo and Ma, Ailong and Tan, Xicheng and Zhang, Liangpei},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={193},
pages={164--186},
year={2022},
publisher={Elsevier}
}
@article{tian2023temporal,
title={Temporal-agnostic change region proposal for semantic change detection},
author={Tian, Shiqi and Tan, Xicheng and Ma, Ailong and Zheng, Zhuo and Zhang, Liangpei and Zhong, Yanfei},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={204},
pages={306--320},
year={2023},
publisher={Elsevier}
}
@article{zheng2022changemask,
title={ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection},
author={Zheng, Zhuo and Zhong, Yanfei and Tian, Shiqi and Ma, Ailong and Zhang, Liangpei},
journal={ISPRS Journal of Photogrammetry and Remote Sensing},
volume={183},
pages={228--239},
year={2022},
publisher={Elsevier}
}
License
The use of Hi-UCD series dataset must follow the licence of open data by Estonian Land Board. Hi-UCD series dataset can be used for academic purpose only, but any commercial use is prohibited.