| --- |
| license: other |
| license_name: license-agreement-on-the-use-of-enmap-data |
| license_link: https://geoservice.dlr.de/resources/licenses/enmap/EnMAP-Data_License_v1_1.pdf |
| --- |
| |
| # How to use it |
| Install Dataset4EO |
|
|
| ```git clone --branch streaming https://github.com/EarthNets/Dataset4EO.git``` |
|
|
| ```pip install -e .``` |
|
|
| Then download the dataset from this Huggingface repo. |
|
|
| ```python |
| import dataset4eo as eodata |
| import litdata as ld |
| |
| |
| train_dataset = eodata.StreamingDataset(input_dir="optimized_enmap_cdl_dataset", num_channels=202, channels_to_select=[0,1,2], shuffle=True, drop_last=True) |
| sample = dataset[101] |
| print(sample.keys()) |
| |
| dataloader = ld.StreamingDataLoader(train_dataset) |
| max_label = 0 |
| for sample in tqdm.tqdm(dataloader): |
| max_id = (np.unique(sample["segmentation_map"])).max() |
| max_label = max_id if max_id > max_label else max_label |
| |
| print(max_label) |
| |
| ``` |
|
|
| The land cover classes of the [dataset](https://www.nass.usda.gov/Research_and_Science/Cropland/sarsfaqs2.php): |
|
|
| | Code | Land Cover | |
| |------|------------------------------------| |
| | 1 | Corn | |
| | 2 | Cotton | |
| | 3 | Rice | |
| | 4 | Sorghum | |
| | 5 | Soybeans | |
| | 6 | Sunflower | |
| | 10 | Peanuts | |
| | 11 | Tobacco | |
| | 12 | Sweet Corn | |
| | 13 | Pop or Orn Corn | |
| | 14 | Mint | |
| | 21 | Barley | |
| | 22 | Durum Wheat | |
| | 23 | Spring Wheat | |
| | 24 | Winter Wheat | |
| | 25 | Other Small Grains | |
| | 26 | Dbl Crop WinWht/Soybeans | |
| | 27 | Rye | |
| | 28 | Oats | |
| | 29 | Millet | |
| | 30 | Speltz | |
| | 31 | Canola | |
| | 32 | Flaxseed | |
| | 33 | Safflower | |
| | 34 | Rape Seed | |
| | 35 | Mustard | |
| | 36 | Alfalfa | |
| | 37 | Other Hay/Non Alfalfa | |
| | 38 | Camelina | |
| | 39 | Buckwheat | |
| | 41 | Sugarbeets | |
| | 42 | Dry Beans | |
| | 43 | Potatoes | |
| | 44 | Other Crops | |
| | 45 | Sugarcane | |
| | 46 | Sweet Potatoes | |
| | 47 | Misc Vegs & Fruits | |
| | 48 | Watermelons | |
| | 49 | Onions | |
| | 50 | Cucumbers | |
| | 51 | Chick Peas | |
| | 52 | Lentils | |
| | 53 | Peas | |
| | 54 | Tomatoes | |
| | 55 | Caneberries | |
| | 56 | Hops | |
| | 57 | Herbs | |
| | 58 | Clover/Wildflowers | |
| | 59 | Sod/Grass Seed | |
| | 60 | Switchgrass | |
| | 61 | Fallow/Idle Cropland | |
| | 62 | Pasture/Grass | |
| | 63 | Forest | |
| | 64 | Shrubland | |
| | 65 | Barren | |
| | 66 | Cherries | |
| | 67 | Peaches | |
| | 68 | Apples | |
| | 69 | Grapes | |
| | 70 | Christmas Trees | |
| | 71 | Other Tree Crops | |
| | 72 | Citrus | |
| | 74 | Pecans | |
| | 75 | Almonds | |
| | 76 | Walnuts | |
| | 77 | Pears | |
| | 81 | Clouds/No Data | |
| | 82 | Developed | |
| | 83 | Water | |
| | 87 | Wetlands | |
| | 88 | Nonag/Undefined | |
| | 92 | Aquaculture | |
| | 111 | Open Water | |
| | 112 | Perennial Ice/Snow | |
| | 121 | Developed/Open Space | |
| | 122 | Developed/Low Intensity | |
| | 123 | Developed/Med Intensity | |
| | 124 | Developed/High Intensity | |
| | 131 | Barren | |
| | 141 | Deciduous Forest | |
| | 142 | Evergreen Forest | |
| | 143 | Mixed Forest | |
| | 152 | Shrubland | |
| | 176 | Grassland/Pasture | |
| | 190 | Woody Wetlands | |
| | 195 | Herbaceous Wetlands | |
| | 204 | Pistachios | |
| | 205 | Triticale | |
| | 206 | Carrots | |
| | 207 | Asparagus | |
| | 208 | Garlic | |
| | 209 | Cantaloupes | |
| | 210 | Prunes | |
| | 211 | Olives | |
| | 212 | Oranges | |
| | 213 | Honeydew Melons | |
| | 214 | Broccoli | |
| | 215 | Avocados | |
| | 216 | Peppers | |
| | 217 | Pomegranates | |
| | 218 | Nectarines | |
| | 219 | Greens | |
| | 220 | Plums | |
| | 221 | Strawberries | |
| | 222 | Squash | |
| | 223 | Apricots | |
| | 224 | Vetch | |
| | 225 | Dbl Crop WinWht/Corn | |
| | 226 | Dbl Crop Oats/Corn | |
| | 227 | Lettuce | |
| | 228 | Dbl Crop Triticale/Corn | |
| | 229 | Pumpkins | |
| | 230 | Dbl Crop Lettuce/Durum Wht | |
| | 231 | Dbl Crop Lettuce/Cantaloupe | |
| | 232 | Dbl Crop Lettuce/Cotton | |
| | 233 | Dbl Crop Lettuce/Barley | |
| | 234 | Dbl Crop Durum Wht/Sorghum | |
| | 235 | Dbl Crop Barley/Sorghum | |
| | 236 | Dbl Crop WinWht/Sorghum | |
| | 237 | Dbl Crop Barley/Corn | |
| | 238 | Dbl Crop WinWht/Cotton | |
| | 239 | Dbl Crop Soybeans/Cotton | |
| | 240 | Dbl Crop Soybeans/Oats | |
| | 241 | Dbl Crop Corn/Soybeans | |
| | 242 | Blueberries | |
| | 243 | Cabbage | |
| | 244 | Cauliflower | |
| | 245 | Celery | |
| | 246 | Radishes | |
| | 247 | Turnips | |
| | 248 | Eggplants | |
| | 249 | Gourds | |
| | 250 | Cranberries | |
| | 254 | Dbl Crop Barley/Soybeans | |
|
|
|
|
|
|
| We acknowledge and give full credit to the original authors of SpectralEarth for their effort in creating this dataset. |
| The dataset is re-hosted in compliance with its original license to facilitate further research. Please cite the following paper for the creation of the dataset: |
|
|
| ``` |
| @article{braham2024spectralearth, |
| title={SpectralEarth: Training Hyperspectral Foundation Models at Scale}, |
| author={Braham, Nassim Ait Ali and Albrecht, Conrad M and Mairal, Julien and Chanussot, Jocelyn and Wang, Yi and Zhu, Xiao Xiang}, |
| journal={arXiv preprint arXiv:2408.08447}, |
| year={2024} |
| } |
| ``` |
