Datasets:
Update README.md
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README.md
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---
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license: cc-by-4.0
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---
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license: cc-by-4.0
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tags:
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- climate
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pretty_name: aef-eurosat
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size_categories:
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- 10K<n<100K
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---
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This is an export of Google's AlphaEarth embeddings which align with the EuroSAT Sentinel-2 patches from roughly sometime during 2018.
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The code to reproduce the dataset download is below (requires a GEE project and login):
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```python
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import os
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import argparse
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import concurrent.futures
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from concurrent.futures import ThreadPoolExecutor
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from pathlib import Path
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from functools import partial
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import shutil
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import ee
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import xarray as xr
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import rioxarray as rio
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from torchgeo.datasets import EuroSAT
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from tqdm import tqdm
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def quantize_aef(image):
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"""quantize float64 -> uint8"""
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power = 2.0
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scale = 127.5
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min_value = -127
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max_value = 127
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sat = image.abs().pow(ee.Number(1.0).divide(power)).multiply(image.signum())
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snapped = sat.multiply(scale).round()
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return snapped.clamp(min_value, max_value).add(ee.Number(127)).uint8()
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def download_aef(filepath, output, year, collection):
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try:
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raster = rio.open_rasterio(filepath)
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raster = raster.rio.reproject("EPSG:4326")
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bounds = raster.rio.bounds() # must by in EPSG:4326
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startDate = ee.Date.fromYMD(year, 1, 1)
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endDate = startDate.advance(1, "year")
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geometry = ee.Geometry.BBox(*bounds)
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image = (
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collection.filter(ee.Filter.date(startDate, endDate))
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.filter(ee.Filter.bounds(geometry))
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.first()
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)
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ds = xr.open_dataset(
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quantize_aef(image),
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engine="ee",
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geometry=bounds,
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projection=image.select(0).projection(),
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)
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ds = ds.isel(time=0)
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ds = ds.rename({"X": "x", "Y": "y"})
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ds = ds.to_array(dim="band").transpose("band", "y", "x")
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output_path = os.path.join(output, filepath.stem + ".tif")
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ds.rio.to_raster(output_path, driver="COG", compress="deflate", dtype="uint8")
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except Exception as e:
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return filepath, str(e)
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return filepath, None
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def main(args):
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ee.Authenticate()
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ee.Initialize(
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project=args.project,
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opt_url="https://earthengine-highvolume.googleapis.com",
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)
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ee.data.setWorkloadTag(args.workload_tag)
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collection = ee.ImageCollection("GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL")
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filepaths = []
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for split in ["train", "val", "test"]:
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ds = EuroSAT(root=args.root, split=split, download=True, checksum=True)
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filepaths.extend([Path(img) for img, _ in ds.imgs])
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with open("errors.csv", "w") as f:
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f.write("filepath,error\n")
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os.makedirs(args.output, exist_ok=True)
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with ThreadPoolExecutor(max_workers=args.num_workers) as executor:
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func = partial(
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download_aef, output=args.output, year=args.year, collection=collection
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)
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futures = [executor.submit(func, filepath) for filepath in filepaths]
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for future in tqdm(
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concurrent.futures.as_completed(futures), total=len(filepaths)
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):
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filepath, error = future.result()
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if error:
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with open("errors.csv", "a") as f:
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f.write(f"{filepath},{error}\n")
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# Reorganize images into folders (optional)
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images = list(Path(args.output).glob("*.tif"))
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print(len(images))
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for image in tqdm(images):
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folder = image.parent / image.stem.split("_")[0]
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folder.mkdir(parents=True, exist_ok=True)
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shutil.move(image, folder / image.name)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--year", type=int, default=2018)
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parser.add_argument("--output", type=str, default="eurosat-aef")
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parser.add_argument("--num_workers", type=int, default=32)
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parser.add_argument("--root", type=str, default="data")
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parser.add_argument("--project", type=str)
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parser.add_argument("--workload_tag", type=str, default="aef-eurosat")
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args = parser.parse_args()
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main(args)
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```
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