README.md

metadata

license: cc-by-4.0
tags:
  - climate
pretty_name: aef-eurosat
size_categories:
  - 10K<n<100K

This is an export of Google's AlphaEarth embeddings which align with the EuroSAT Sentinel-2 patches from roughly sometime during 2018.

The code to reproduce the dataset download is below (requires a GEE project and login):

import os
import argparse
import concurrent.futures
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
from functools import partial
import shutil

import ee
import xarray as xr
import rioxarray as rio
from torchgeo.datasets import EuroSAT
from tqdm import tqdm


def quantize_aef(image):
    """quantize float64 -> uint8"""
    power = 2.0
    scale = 127.5
    min_value = -127
    max_value = 127

    sat = image.abs().pow(ee.Number(1.0).divide(power)).multiply(image.signum())
    snapped = sat.multiply(scale).round()
    return snapped.clamp(min_value, max_value).add(ee.Number(127)).uint8()


def download_aef(filepath, output, year, collection):
    try:
        raster = rio.open_rasterio(filepath)
        raster = raster.rio.reproject("EPSG:4326")
        bounds = raster.rio.bounds()  # must by in EPSG:4326
        startDate = ee.Date.fromYMD(year, 1, 1)
        endDate = startDate.advance(1, "year")
        geometry = ee.Geometry.BBox(*bounds)
        image = (
            collection.filter(ee.Filter.date(startDate, endDate))
            .filter(ee.Filter.bounds(geometry))
            .first()
        )
        ds = xr.open_dataset(
            quantize_aef(image),
            engine="ee",
            geometry=bounds,
            projection=image.select(0).projection(),
        )
        ds = ds.isel(time=0)
        ds = ds.rename({"X": "x", "Y": "y"})
        ds = ds.to_array(dim="band").transpose("band", "y", "x")
        output_path = os.path.join(output, filepath.stem + ".tif")
        ds.rio.to_raster(output_path, driver="COG", compress="deflate", dtype="uint8")
    except Exception as e:
        return filepath, str(e)
    return filepath, None


def main(args):
    ee.Authenticate()
    ee.Initialize(
        project=args.project,
        opt_url="https://earthengine-highvolume.googleapis.com",
    )
    ee.data.setWorkloadTag(args.workload_tag)
    collection = ee.ImageCollection("GOOGLE/SATELLITE_EMBEDDING/V1/ANNUAL")

    filepaths = []
    for split in ["train", "val", "test"]:
        ds = EuroSAT(root=args.root, split=split, download=True, checksum=True)
        filepaths.extend([Path(img) for img, _ in ds.imgs])

    with open("errors.csv", "w") as f:
        f.write("filepath,error\n")

    os.makedirs(args.output, exist_ok=True)

    with ThreadPoolExecutor(max_workers=args.num_workers) as executor:
        func = partial(
            download_aef, output=args.output, year=args.year, collection=collection
        )
        futures = [executor.submit(func, filepath) for filepath in filepaths]

        for future in tqdm(
            concurrent.futures.as_completed(futures), total=len(filepaths)
        ):
            filepath, error = future.result()
            if error:
                with open("errors.csv", "a") as f:
                    f.write(f"{filepath},{error}\n")

    # Reorganize images into folders (optional)
    images = list(Path(args.output).glob("*.tif"))
    print(len(images))
    for image in tqdm(images):
        folder = image.parent / image.stem.split("_")[0]
        folder.mkdir(parents=True, exist_ok=True)
        shutil.move(image, folder / image.name)


if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument("--year", type=int, default=2018)
    parser.add_argument("--output", type=str, default="eurosat-aef")
    parser.add_argument("--num_workers", type=int, default=32)
    parser.add_argument("--root", type=str, default="data")
    parser.add_argument("--project", type=str)
    parser.add_argument("--workload_tag", type=str, default="aef-eurosat")
    args = parser.parse_args()
    main(args)