Shortcuts

Source code for torchgeo.datasets.eurosat

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""EuroSAT dataset."""

import os
from typing import Callable, Dict, Optional

from torch import Tensor

from .geo import VisionClassificationDataset
from .utils import check_integrity, download_url, extract_archive, rasterio_loader


class EuroSAT(VisionClassificationDataset):
    """EuroSAT dataset.

    The `EuroSAT <https://github.com/phelber/EuroSAT>`_ dataset is based on Sentinel-2
    satellite images covering 13 spectral bands and consists of 10 target classes with
    a total of 27,000 labeled and geo-referenced images.

    Dataset format:

    * rasters are 13-channel GeoTiffs
    * labels are values in the range [0,9]

    Dataset classes:

    * Industrial Buildings
    * Residential Buildings
    * Annual Crop
    * Permanent Crop
    * River
    * Sea and Lake
    * Herbaceous Vegetation
    * Highway
    * Pasture
    * Forest

    This dataset uses the train/val/test splits defined in the "In-domain representation
    learning for remote sensing" paper:

    * https://arxiv.org/abs/1911.06721

    If you use this dataset in your research, please cite the following papers:

    * https://ieeexplore.ieee.org/document/8736785
    * https://ieeexplore.ieee.org/document/8519248
    """

    url = "http://madm.dfki.de/files/sentinel/EuroSATallBands.zip"  # 2.0 GB download
    filename = "EuroSATallBands.zip"
    md5 = "5ac12b3b2557aa56e1826e981e8e200e"

    # For some reason the class directories are actually nested in this directory
    base_dir = os.path.join(
        "ds", "images", "remote_sensing", "otherDatasets", "sentinel_2", "tif"
    )

    splits = ["train", "val", "test"]
    split_urls = {
        "train": "https://storage.googleapis.com/remote_sensing_representations/eurosat-train.txt",  # noqa: E501
        "val": "https://storage.googleapis.com/remote_sensing_representations/eurosat-val.txt",  # noqa: E501
        "test": "https://storage.googleapis.com/remote_sensing_representations/eurosat-test.txt",  # noqa: E501
    }
    split_md5s = {
        "train": "908f142e73d6acdf3f482c5e80d851b1",
        "val": "95de90f2aa998f70a3b2416bfe0687b4",
        "test": "7ae5ab94471417b6e315763121e67c5f",
    }

[docs] def __init__( self, root: str = "data", split: str = "train", transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new EuroSAT dataset instance. Args: root: root directory where dataset can be found split: one of "train", "val", or "test" transforms: a function/transform that takes input sample and its target as entry and returns a transformed version download: if True, download dataset and store it in the root directory checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: RuntimeError: if ``download=False`` and data is not found, or checksums don't match """ self.root = root self.transforms = transforms self.download = download self.checksum = checksum self._verify() valid_fns = set() with open(os.path.join(self.root, f"eurosat-{split}.txt"), "r") as f: for fn in f: valid_fns.add(fn.strip().replace(".jpg", ".tif")) is_in_split: Callable[[str], bool] = lambda x: os.path.basename(x) in valid_fns super().__init__( root=os.path.join(root, self.base_dir), transforms=transforms, loader=rasterio_loader, is_valid_file=is_in_split, )
def _check_integrity(self) -> bool: """Check integrity of dataset. Returns: True if dataset files are found and/or MD5s match, else False """ integrity: bool = check_integrity( os.path.join(self.root, self.filename), self.md5 if self.checksum else None ) return integrity def _verify(self) -> None: """Verify the integrity of the dataset. Raises: RuntimeError: if ``download=False`` but dataset is missing or checksum fails """ # Check if the files already exist filepath = os.path.join(self.root, self.base_dir) if os.path.exists(filepath): return # Check if zip file already exists (if so then extract) if self._check_integrity(): self._extract() return # Check if the user requested to download the dataset if not self.download: raise RuntimeError( "Dataset not found in `root` directory and `download=False`, " "either specify a different `root` directory or use `download=True` " "to automaticaly download the dataset." ) # Download and extract the dataset self._download() self._extract() def _download(self) -> None: """Download the dataset.""" download_url( self.url, self.root, filename=self.filename, md5=self.md5 if self.checksum else None, ) for split in self.splits: download_url( self.split_urls[split], self.root, filename=f"eurosat-{split}.txt", md5=self.split_md5s[split] if self.checksum else None, ) def _extract(self) -> None: """Extract the dataset.""" filepath = os.path.join(self.root, self.filename) extract_archive(filepath)

© Copyright 2021, Microsoft Corporation. Revision c2b56148.

Built with Sphinx using a theme provided by Read the Docs.
Read the Docs v: v0.1.1
Versions
latest
stable
v0.1.1
v0.1.0
Downloads
On Read the Docs
Project Home
Builds

Free document hosting provided by Read the Docs.

Docs

Access comprehensive developer documentation for PyTorch

View Docs

Tutorials

Get in-depth tutorials for beginners and advanced developers

View Tutorials

Resources

Find development resources and get your questions answered

View Resources