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array 3D | y
int16 0
19
|
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[[[145.0,168.0,303.0,294.0,521.0,1273.0,1476.0,1461.0,1700.0,2164.0,1107.0,583.0],[137.0,154.0,299.0(...TRUNCATED) | 6 |
[[[535.0,574.0,667.0,771.0,1118.0,1482.0,1696.0,1955.0,1898.0,1978.0,1621.0,1081.0],[497.0,573.0,705(...TRUNCATED) | 7 |
[[[394.0,310.0,464.0,427.0,1144.0,1977.0,2432.0,1994.0,2704.0,2796.0,1931.0,1165.0],[360.0,384.0,696(...TRUNCATED) | 18 |
[[[294.0,356.0,774.0,692.0,1472.0,2222.0,2468.0,2852.0,2644.0,2412.0,2208.0,1464.0],[317.0,366.0,783(...TRUNCATED) | 4 |
[[[173.0,257.0,434.0,528.0,1224.0,1868.0,2023.0,1752.0,2404.0,2420.0,2080.0,1283.0],[176.0,263.0,460(...TRUNCATED) | 9 |
[[[250.0,336.0,467.0,620.0,728.0,853.0,919.0,957.0,969.0,1598.0,1145.0,952.0],[320.0,359.0,509.0,651(...TRUNCATED) | 12 |
[[[618.0,701.0,996.0,1046.0,1820.0,2860.0,3124.0,3516.0,3236.0,2952.0,2256.0,1395.0],[579.0,666.0,96(...TRUNCATED) | 5 |
[[[532.0,658.0,928.0,877.0,1305.0,2116.0,2250.0,2576.0,2400.0,2352.0,2016.0,1420.0],[217.0,347.0,662(...TRUNCATED) | 16 |
[[[352.0,358.0,628.0,710.0,973.0,1213.0,1332.0,1382.0,1407.0,1710.0,1826.0,1320.0],[195.0,306.0,637.(...TRUNCATED) | 16 |
[[[247.0,221.0,633.0,291.0,1178.0,3190.0,3736.0,4804.0,3904.0,4628.0,1798.0,1018.0],[278.0,242.0,668(...TRUNCATED) | 15 |
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Dataset Card for FranceCrops
Dataset Summary
FranceCrops is a satellite imagery time series dataset for crop classification in France.
Each sample in the dataset consists of:
- Input Features (
x
): 3D arrays of shape (100, 60, 12) in float16:- 100: number of timeseries sampled within an agricultural field
- 60: temporal dimension (measurements every 5 days from 01/02/2022 to 30/11/2022)
- 12: spectral bands from Sentinel-2 satellite
- Labels (
y
): Integer class labels (int16), one of 20 crop types
Splits
🚧 Work in Progress 🚧
train
: 20,000 samples (~3.4GB)
Usage
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("saget-antoine/francecrops", split="train")
# Example of accessing a single sample
sample = dataset[0]
x = sample["x"] # Get the input features
y = sample["y"] # Get the label
Typical usage with PyTorch:
import torch
from torch.utils.data import DataLoader
from datasets import load_dataset
dataset = load_dataset("saget-antoine/francecrops", split="train").with_format("torch", columns=["x", "y"], dtype=torch.float16)
train_loader = DataLoader(dataset, batch_size=1024, shuffle=True, num_workers=8)
Dataset Creation
Source Data
🚧 Work in Progress 🚧
- Features: Sentinel-2 L2A satellite imagery extracted using GEE
- Labels: Crop type classification from the 2022French Registre Parcellaire Graphique (RPG)
Preprocessing
🚧 Work in Progress 🚧
- Remove clouds, shadows, and missing data time steps
- Temporal alignment
- Temporal interpolation when missing
License
Creative Commons Attribution-ShareAlike 4.0 International (CC-BY-SA-4.0)
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