Datasets:
antoinesaget
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Add train split and README.md
Browse files- README.md +79 -0
- train/data-00000-of-00004.arrow +3 -0
- train/data-00001-of-00004.arrow +3 -0
- train/data-00002-of-00004.arrow +3 -0
- train/data-00003-of-00004.arrow +3 -0
- train/dataset_info.json +48 -0
- train/state.json +22 -0
README.md
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---
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pretty_name: FranceCrops
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license: cc-by-sa-4.0
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tags:
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- earth-observation
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- remote-sensing
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- agriculture
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- timeseries
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- geospatial
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task_categories:
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- image-classification
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size_categories:
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- 10K<n<100K
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---
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# Dataset Card for FranceCrops
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## Dataset Summary
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FranceCrops is a satellite imagery time series dataset for crop classification in France.
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Each sample in the dataset consists of:
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- **Input Features (`x`)**: 3D arrays of shape (100, 60, 12) in float16:
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- 100: number of timeseries sampled within an agricultural field
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- 60: temporal dimension (measurements every 5 days from 01/02/2022 to 30/11/2022)
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- 12: spectral bands from Sentinel-2 satellite
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- **Labels (`y`)**: Integer class labels (int16), one of 20 crop types
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## Splits
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🚧 Work in Progress 🚧
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- `train`: 20,000 samples (~3.4GB)
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## Usage
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```python
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from datasets import load_dataset
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# Load the dataset
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dataset = load_dataset("saget-antoine/francecrops", split="train")
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# Example of accessing a single sample
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sample = dataset[0]
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x = sample["x"] # Get the input features
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y = sample["y"] # Get the label
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```
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Typical usage with PyTorch:
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```python
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import torch
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from torch.utils.data import DataLoader
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from datasets import load_dataset
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dataset = load_dataset("saget-antoine/francecrops", split="train").with_format("torch", columns=["x", "y"], dtype=torch.float16)
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train_loader = DataLoader(dataset, batch_size=1024, shuffle=True, num_workers=8)
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```
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## Dataset Creation
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### Source Data
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🚧 Work in Progress 🚧
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- Features: Sentinel-2 L2A satellite imagery extracted using GEE
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- Labels: Crop type classification from the 2022French Registre Parcellaire Graphique (RPG)
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### Preprocessing
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🚧 Work in Progress 🚧
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- Remove clouds, shadows, and missing data time steps
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- Temporal alignment
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- Temporal interpolation when missing
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## License
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Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA-4.0)
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train/data-00000-of-00004.arrow
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size 842032512
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train/data-00001-of-00004.arrow
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size 842032512
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train/data-00002-of-00004.arrow
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size 842032512
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train/data-00003-of-00004.arrow
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version https://git-lfs.github.com/spec/v1
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size 842032512
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train/dataset_info.json
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{
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"builder_name": "",
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"citation": "",
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"config_name": "",
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"dataset_name": "francecrops",
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"dataset_size": 3368120000,
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"description": "Francecrops train dataset with 20 classes and 1000 samples per class",
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"download_checksums": {},
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"download_size": 0,
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"features": {
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"x": {
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"shape": [
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100,
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60,
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12
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],
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"dtype": "float16",
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"_type": "Array3D"
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},
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"y": {
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"dtype": "int16",
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"_type": "Value"
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}
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},
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"homepage": "",
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"license": "CC-BY-SA-4.0",
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"size_in_bytes": 3368120000,
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"splits": {
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"train": {
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"name": "train",
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"num_bytes": 3368120000,
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"num_examples": 20000,
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"shard_lengths": [
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5000,
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5000,
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5000,
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5000
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],
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"dataset_name": "francecrops"
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}
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},
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"version": {
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"version_str": "0.0.1",
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"major": 0,
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"minor": 0,
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"patch": 1
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}
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}
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train/state.json
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{
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"_data_files": [
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{
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"filename": "data-00000-of-00004.arrow"
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},
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{
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"filename": "data-00001-of-00004.arrow"
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},
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{
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"filename": "data-00002-of-00004.arrow"
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},
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{
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"filename": "data-00003-of-00004.arrow"
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}
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],
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"_fingerprint": "f81e340b36ebafff",
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"_format_columns": null,
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"_format_kwargs": {},
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"_format_type": null,
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"_output_all_columns": false,
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"_split": "train"
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}
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