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license: cc-by-4.0 |
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# Dataset Card for CDL Crop Types |
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## Dataset Description |
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- **Homepage: https://huggingface.co/datasets/ibm-nasa-geospatial/cdl-crops/** |
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- **Point of Contact: Dr. Hamed Alemohammad (halemohammad@clarku.edu)** |
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### Dataset Summary |
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This dataset contains temporal Harmonized Landsat-Sentinel imagery of diverse land cover and crop type classes across the contiguous United States for the year 2022. The target labels are derived from USDA's Crop Data Layer. It's primary purpose is for training geospatial machine learning models. |
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## Dataset Structure |
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## TIFF Metadata |
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Each tiff file cover a 224 x 224 pixel area at 30m spatial resolution. Each input satellite file contains 18 bands including 6 spectral bands for three time steps stacked together. Each GeoTIFF file for the mask contains one band with the classes for each pixel. |
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## Band Order |
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For scenes: |
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Channel, Name, HLS S30 Band number |
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1, Blue, B02 |
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2, Green, B03 |
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3, Red, B04 |
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4, NIR, B8A |
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5, SW 1, B11 |
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6, SW 2, B12 |
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Masks are a single band with values: |
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0 : "No Data" |
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1 : "Natural Vegetation" |
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2 : "Forest" |
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3 : "Corn" |
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4 : "Soybeans" |
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5 : "Wetlands" |
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6 : "Developed/Barren" |
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7 : "Open Water" |
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8 : "Winter Wheat" |
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9 : "Alfalfa" |
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10 : "Fallow/Idle Cropland" |
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11 : "Cotton" |
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12 : "Sorghum" |
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13 : "Other" |
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## Class Distribution |
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## Data Splits |
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The 3,854 chips have been randomly split into training (80%) and validation (20%) and corresponding ids recorded in cvs files `train_ids.csv` and `val_ids.csv`. |
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## Dataset Creation |
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