| --- |
| license: cc-by-4.0 |
| task_categories: |
| - image-to-image |
| tags: |
| - remote-sensing |
| - cloud-removal |
| - SAR |
| - sentinel |
| pretty_name: SEN12MS-CR |
| citation: | |
| @article{meraner2020cloud, |
| title={Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion}, |
| author={Meraner, Andrea and Ebel, Patrick and Zhu, Xiao Xiang and Schmitt, Michael}, |
| journal={ISPRS Journal of Photogrammetry and Remote Sensing}, |
| volume={166}, |
| pages={333--346}, |
| year={2020} |
| } |
| size_categories: |
| - 100K<n<1M |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: |
| - spring/scene_1.parquet |
| - spring/scene_6.parquet |
| - spring/scene_8.parquet |
| - spring/scene_9.parquet |
| - spring/scene_15.parquet |
| - spring/scene_21.parquet |
| - spring/scene_26.parquet |
| - spring/scene_39.parquet |
| - spring/scene_40.parquet |
| - spring/scene_45.parquet |
| - spring/scene_58.parquet |
| - spring/scene_63.parquet |
| - spring/scene_66.parquet |
| - spring/scene_75.parquet |
| - spring/scene_77.parquet |
| - spring/scene_97.parquet |
| - spring/scene_100.parquet |
| - spring/scene_101.parquet |
| - spring/scene_109.parquet |
| - spring/scene_110.parquet |
| - spring/scene_113.parquet |
| - spring/scene_115.parquet |
| - spring/scene_117.parquet |
| - spring/scene_119.parquet |
| - spring/scene_120.parquet |
| - spring/scene_121.parquet |
| - spring/scene_124.parquet |
| - spring/scene_126.parquet |
| - spring/scene_128.parquet |
| - spring/scene_132.parquet |
| - spring/scene_134.parquet |
| - spring/scene_141.parquet |
| - spring/scene_142.parquet |
| - spring/scene_145.parquet |
| - spring/scene_147.parquet |
| - summer/scene_4.parquet |
| - summer/scene_7.parquet |
| - summer/scene_11.parquet |
| - summer/scene_15.parquet |
| - summer/scene_25.parquet |
| - summer/scene_27.parquet |
| - summer/scene_31.parquet |
| - summer/scene_36.parquet |
| - summer/scene_40.parquet |
| - summer/scene_42.parquet |
| - summer/scene_43.parquet |
| - summer/scene_47.parquet |
| - summer/scene_55.parquet |
| - summer/scene_56.parquet |
| - summer/scene_72.parquet |
| - summer/scene_76.parquet |
| - summer/scene_86.parquet |
| - summer/scene_87.parquet |
| - summer/scene_89.parquet |
| - summer/scene_90.parquet |
| - summer/scene_93.parquet |
| - summer/scene_95.parquet |
| - summer/scene_100.parquet |
| - summer/scene_101.parquet |
| - summer/scene_102.parquet |
| - summer/scene_113.parquet |
| - summer/scene_114.parquet |
| - summer/scene_115.parquet |
| - summer/scene_120.parquet |
| - summer/scene_121.parquet |
| - summer/scene_123.parquet |
| - summer/scene_124.parquet |
| - summer/scene_125.parquet |
| - summer/scene_126.parquet |
| - summer/scene_132.parquet |
| - summer/scene_133.parquet |
| - summer/scene_135.parquet |
| - summer/scene_137.parquet |
| - summer/scene_139.parquet |
| - summer/scene_140.parquet |
| - summer/scene_143.parquet |
| - summer/scene_146.parquet |
| - summer/scene_147.parquet |
| - fall/scene_1.parquet |
| - fall/scene_3.parquet |
| - fall/scene_4.parquet |
| - fall/scene_6.parquet |
| - fall/scene_11.parquet |
| - fall/scene_14.parquet |
| - fall/scene_19.parquet |
| - fall/scene_22.parquet |
| - fall/scene_26.parquet |
| - fall/scene_27.parquet |
| - fall/scene_28.parquet |
| - fall/scene_30.parquet |
| - fall/scene_31.parquet |
| - fall/scene_33.parquet |
| - fall/scene_35.parquet |
| - fall/scene_37.parquet |
| - fall/scene_39.parquet |
| - fall/scene_40.parquet |
| - fall/scene_41.parquet |
| - fall/scene_42.parquet |
| - fall/scene_57.parquet |
| - fall/scene_64.parquet |
| - fall/scene_71.parquet |
| - fall/scene_77.parquet |
| - fall/scene_81.parquet |
| - fall/scene_82.parquet |
| - fall/scene_83.parquet |
| - fall/scene_85.parquet |
| - fall/scene_88.parquet |
| - fall/scene_91.parquet |
| - fall/scene_93.parquet |
| - fall/scene_100.parquet |
| - fall/scene_104.parquet |
| - fall/scene_105.parquet |
| - fall/scene_107.parquet |
| - fall/scene_109.parquet |
| - fall/scene_110.parquet |
| - fall/scene_112.parquet |
| - fall/scene_114.parquet |
| - fall/scene_116.parquet |
| - fall/scene_119.parquet |
| - fall/scene_120.parquet |
| - fall/scene_122.parquet |
| - fall/scene_125.parquet |
| - fall/scene_128.parquet |
| - fall/scene_131.parquet |
| - fall/scene_133.parquet |
| - fall/scene_134.parquet |
| - fall/scene_135.parquet |
| - fall/scene_136.parquet |
| - fall/scene_141.parquet |
| - fall/scene_142.parquet |
| - fall/scene_144.parquet |
| - fall/scene_147.parquet |
| - fall/scene_148.parquet |
| - fall/scene_149.parquet |
| - winter/scene_8.parquet |
| - winter/scene_21.parquet |
| - winter/scene_25.parquet |
| - winter/scene_42.parquet |
| - winter/scene_47.parquet |
| - winter/scene_49.parquet |
| - winter/scene_55.parquet |
| - winter/scene_59.parquet |
| - winter/scene_61.parquet |
| - winter/scene_62.parquet |
| - winter/scene_64.parquet |
| - winter/scene_68.parquet |
| - winter/scene_75.parquet |
| - winter/scene_81.parquet |
| - winter/scene_94.parquet |
| - winter/scene_102.parquet |
| - winter/scene_104.parquet |
| - winter/scene_112.parquet |
| - winter/scene_116.parquet |
| - winter/scene_135.parquet |
| - winter/scene_146.parquet |
| - split: validation |
| path: |
| - spring/scene_17.parquet |
| - summer/scene_17.parquet |
| - summer/scene_19.parquet |
| - summer/scene_80.parquet |
| - summer/scene_127.parquet |
| - fall/scene_65.parquet |
| - winter/scene_22.parquet |
| - winter/scene_84.parquet |
| - winter/scene_107.parquet |
| - winter/scene_130.parquet |
| - split: test |
| path: |
| - spring/scene_31.parquet |
| - spring/scene_44.parquet |
| - spring/scene_106.parquet |
| - spring/scene_123.parquet |
| - spring/scene_140.parquet |
| - summer/scene_73.parquet |
| - summer/scene_119.parquet |
| - fall/scene_139.parquet |
| - winter/scene_63.parquet |
| - winter/scene_108.parquet |
| --- |
| |
| # SEN12MS-CR |
|
|
| Reorganized mirror of the [SEN12MS-CR dataset](https://mediatum.ub.tum.de/1554803) in Parquet format. |
|
|
| ## Quick Start |
|
|
| ```python |
| from datasets import load_dataset |
| import numpy as np |
| |
| ds = load_dataset("Hermanni/sen12mscr", streaming=True) |
| |
| for sample in ds["train"]: |
| sar = np.frombuffer(sample["sar"], dtype=np.float32).reshape(sample["sar_shape"]) |
| cloudy = np.frombuffer(sample["cloudy"], dtype=np.int16).reshape(sample["opt_shape"]) |
| target = np.frombuffer(sample["target"], dtype=np.int16).reshape(sample["opt_shape"]) |
| |
| # Optical tensors are stored as HWC: (256, 256, 13) |
| # Convert to CHW if needed: |
| # cloudy = np.transpose(cloudy, (2, 0, 1)) |
| # target = np.transpose(target, (2, 0, 1)) |
| break |
| ``` |
|
|
| ## Notes |
|
|
| - sar is stored as float32 |
| - cloudy and target are stored as int16 |
| - opt_shape is stored in HWC order, typically (256, 256, 13) |
| - The dtype column is a legacy field and should not be used for decoding cloudy or target |
| |
| ## Full Download |
| |
| ```python |
| ds = load_dataset("Hermanni/sen12mscr", split="train") |
| ``` |
| |
| ## PyTorch Example |
| |
| ```python |
| from torch.utils.data import Dataset, DataLoader |
| from datasets import load_dataset |
| import numpy as np |
| import torch |
| |
| class SEN12MSCR(Dataset): |
| def __init__(self, hf_dataset, normalize=True, chw_optical=True): |
| self.ds = hf_dataset |
| self.normalize = normalize |
| self.chw_optical = chw_optical |
| |
| def __len__(self): |
| return len(self.ds) |
| |
| def __getitem__(self, idx): |
| s = self.ds[idx] |
| |
| sar = np.frombuffer(s["sar"], dtype=np.float32).reshape(s["sar_shape"]).astype(np.float32) |
| cloudy = np.frombuffer(s["cloudy"], dtype=np.int16).reshape(s["opt_shape"]).astype(np.float32) |
| target = np.frombuffer(s["target"], dtype=np.int16).reshape(s["opt_shape"]).astype(np.float32) |
| |
| if self.chw_optical: |
| cloudy = np.transpose(cloudy, (2, 0, 1)) |
| target = np.transpose(target, (2, 0, 1)) |
| |
| sar = torch.from_numpy(sar.copy()) |
| cloudy = torch.from_numpy(cloudy.copy()) |
| target = torch.from_numpy(target.copy()) |
| |
| if self.normalize: |
| cloudy /= 10000.0 |
| target /= 10000.0 |
| |
| return {"sar": sar, "cloudy": cloudy, "target": target} |
| |
| ds = load_dataset("Hermanni/sen12mscr", split="train") |
| loader = DataLoader(SEN12MSCR(ds), batch_size=8, shuffle=True, num_workers=4) |
| ``` |
| |
| ## Contents |
| |
| - ~122,218 triplets |
| - SAR: Sentinel-1, 2 channels, float32 |
| - Cloudy: Sentinel-2, 13 channels, int16 |
| - Target: Sentinel-2, 13 channels, int16 |
| - 4 seasons, 175 global ROIs (2018) |
| |
| ## Columns |
| |
| | Column | Type | Description | |
| |---|---|---| |
| | sar | binary | SAR bytes, decode as float32, reshape with sar_shape | |
| | cloudy | binary | Cloudy S2 bytes, decode as int16, reshape with opt_shape | |
| | target | binary | Cloud-free S2 bytes, decode as int16, reshape with opt_shape | |
| | sar_shape | list[int] | SAR shape, typically [2, 256, 256] | |
| | opt_shape | list[int] | Optical shape, typically [256, 256, 13] | |
| | dtype | string | Legacy field from SAR export; do not use for optical decoding | |
| | season | string | spring / summer / fall / winter | |
| | scene | string | Scene number | |
| | patch | string | Patch ID | |
|
|
| ## License |
|
|
| CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) |
|
|
|
|
| ## Source |
|
|
| - mediaTUM (ID: 1554803) (https://mediatum.ub.tum.de/1554803) |
| |
|
|
