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WHUS2-CD+ dataset for sentinel-2 cloud detection validation

WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland. If you use this dataset for your research, please cite us accordingly

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Creatorjun li
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Published2021-09-16
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DOI10.5281/zenodo.5511793
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Downloads11,408
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Licensecc-by-4.0
File Size27.8 GB
Data TypeDataset
Published2021
Licensecc-by-4.0
Total Views2,567
Total Downloads11,408

WHUS2-CD+ is a cloud validation detection dataset for Sentinel-2A images. WHUS2-CD+ contains 36 manually labeled cloud masks at 10m resolution and corresponding Sentinel-2A images evenly distributed over China mainland.

If you use this dataset for your research, please cite us accordingly:

#Reference: 

[1] J. Li, Z. Wu, Z. Hu, C. Jian, S. Luo, L. Mou, X. Zhu, and M. Molinier, "A lightweight deep learning based cloud detection method for Sentinel-2A imagery fusing multi-scale spectral and spatial features," in IEEE Transactions on Geoscience and Remote Sensing, 2021. https://doi.org/10.1109/TGRS.2021.3069641.

[2] Z. Wu, J. Li, Y. Wang, Z. Hu and M. Molinier, "Self-Attentive Generative Adversarial Network for Cloud Detection in High Resolution Remote Sensing Images," in IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 10, pp. 1792-1796, Oct. 2020. https://doi.org/10.1109/LGRS.2019.2955071.

The training and testing list is (The challenging senes are marked in bold):

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WHUS2-CD+ dataset for sentinel-2 cloud detection validation (Full Dataset)27.8 GB
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ReadmeVia DOI record
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Files are hosted on the source repository. Click download to access the full dataset.

jun li (2021). WHUS2-CD+ dataset for sentinel-2 cloud detection validation. https://doi.org/10.5281/zenodo.5511793
Training set
S2A_MSIL1C_20190714T043711_N0208_R033_T46TFN_20190714T073938
S2A_MSIL1C_20191219T040151_N0208_R004_T47SQU_20191219T055033
S2A_MSIL1C_20190630T045701_N0207_R119_T45SWC_20190630T080543
S2A_MSIL1C_20191215T042151_N0208_R090_T46RGV_20191215T065406
S2A_MSIL1C_20180930T044701_N0206_R076_T45SXR_20180930T074413
S2A_MSIL1C_20200317T024541_N0209_R132_T51TWM_20200317T053350
S2A_MSIL1C_20180816T053641_N0206_R005_T44TKK_20180816T093424
S2A_MSIL1C_20191023T040821_N0208_R047_T47TQF_20191023T074550
S2A_MSIL1C_20180824T031541_N0206_R118_T50TKL_20180824T061636
S2A_MSIL1C_20191118T025011_N0208_R132_T50RMN_20191118T071843
S2A_MSIL1C_20190916T023551_N0208_R089_T50RQS_20190916T042547
S2A_MSIL1C_20190819T031541_N0208_R118_T49SFU_20190819T065332
S2A_MSIL1C_20190815T051651_N0208_R062_T44TPN_20190815T090034
S2A_MSIL1C_20200410T022551_N0209_R046_T51TXG_20200410T042047
S2A_MSIL1C_20191002T025551_N0208_R032_T50TQQ_20191002T054113
S2A_MSIL1C_20180429T032541_N0206_R018_T49SCV_20180429T062304
S2A_MSIL1C_20200506T024551_N0209_R132_T51UWS_20200506T043639
S2A_MSIL1C_20200325T034531_N0209_R104_T47RQL_20200325T065315
S2A_MSIL1C_20190928T031541_N0208_R118_T49RBJ_20190928T061248
S2A_MSIL1C_20180827T032541_N0206_R018_T48RYV_20180827T062627