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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.
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:
| 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}
}
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