Source code for torchgeo.datasets.eurosat

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

"""EuroSAT dataset."""

import os
from import Sequence
from typing import Callable, Optional, cast

import matplotlib.pyplot as plt
import numpy as np
import torch
from matplotlib.figure import Figure
from torch import Tensor

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

[docs]class EuroSAT(NonGeoClassificationDataset): """EuroSAT dataset. The `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: * Annual Crop * Forest * Herbaceous Vegetation * Highway * Industrial Buildings * Pasture * Permanent Crop * Residential Buildings * River * SeaLake This dataset uses the train/val/test splits defined in the "In-domain representation learning for remote sensing" paper: * If you use this dataset in your research, please cite the following papers: * * """ url = "" # noqa: E501 filename = "" 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": "", # noqa: E501 "val": "", # noqa: E501 "test": "", # noqa: E501 } split_md5s = { "train": "908f142e73d6acdf3f482c5e80d851b1", "val": "95de90f2aa998f70a3b2416bfe0687b4", "test": "7ae5ab94471417b6e315763121e67c5f", } all_band_names = ( "B01", "B02", "B03", "B04", "B05", "B06", "B07", "B08", "B08A", "B09", "B10", "B11", "B12", ) rgb_bands = ("B04", "B03", "B02") BAND_SETS = {"all": all_band_names, "rgb": rgb_bands}
[docs] def __init__( self, root: str = "data", split: str = "train", bands: Sequence[str] = BAND_SETS["all"], 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" bands: a sequence of band names to load 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: AssertionError: if ``split`` argument is invalid RuntimeError: if ``download=False`` and data is not found, or checksums don't match .. versionadded:: 0.3 The *bands* parameter. """ self.root = root self.transforms = transforms = download self.checksum = checksum assert split in ["train", "val", "test"] self._validate_bands(bands) self.bands = bands self.band_indices = Tensor( [self.all_band_names.index(b) for b in bands if b in self.all_band_names] ).long() self._verify() valid_fns = set() with open(os.path.join(self.root, f"eurosat-{split}.txt")) 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, )
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]: """Return an index within the dataset. Args: index: index to return Returns: data and label at that index """ image, label = self._load_image(index) image = torch.index_select(image, dim=0, index=self.band_indices).float() sample = {"image": image, "label": label} if self.transforms is not None: sample = self.transforms(sample) return sample
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 raise RuntimeError( "Dataset not found in `root` directory and `download=False`, " "either specify a different `root` directory or use `download=True` " "to automatically 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) def _validate_bands(self, bands: Sequence[str]) -> None: """Validate list of bands. Args: bands: user-provided sequence of bands to load Raises: AssertionError: if ``bands`` is not a sequence ValueError: if an invalid band name is provided .. versionadded:: 0.3 """ assert isinstance(bands, Sequence), "'bands' must be a sequence" for band in bands: if band not in self.all_band_names: raise ValueError(f"'{band}' is an invalid band name.")
[docs] def plot( self, sample: dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, ) -> Figure: """Plot a sample from the dataset. Args: sample: a sample returned by :meth:`NonGeoClassificationDataset.__getitem__` show_titles: flag indicating whether to show titles above each panel suptitle: optional string to use as a suptitle Returns: a matplotlib Figure with the rendered sample Raises: ValueError: if RGB bands are not found in dataset .. versionadded:: 0.2 """ rgb_indices = [] for band in self.rgb_bands: if band in self.bands: rgb_indices.append(self.bands.index(band)) else: raise ValueError("Dataset doesn't contain some of the RGB bands") image = np.take(sample["image"].numpy(), indices=rgb_indices, axis=0) image = np.rollaxis(image, 0, 3) image = np.clip(image / 3000, 0, 1) label = cast(int, sample["label"].item()) label_class = self.classes[label] showing_predictions = "prediction" in sample if showing_predictions: prediction = cast(int, sample["prediction"].item()) prediction_class = self.classes[prediction] fig, ax = plt.subplots(figsize=(4, 4)) ax.imshow(image) ax.axis("off") if show_titles: title = f"Label: {label_class}" if showing_predictions: title += f"\nPrediction: {prediction_class}" ax.set_title(title) if suptitle is not None: plt.suptitle(suptitle) return fig
[docs]class EuroSAT100(EuroSAT): """Subset of EuroSAT containing only 100 images. Intended for tutorials and demonstrations, not for benchmarking. Maintains the same file structure, classes, and train-val-test split. Each class has 10 images (6 train, 2 val, 2 test), for a total of 100 images. .. versionadded:: 0.5 """ url = "" filename = "" md5 = "c21c649ba747e86eda813407ef17d596" split_urls = { "train": "", # noqa: E501 "val": "", # noqa: E501 "test": "", # noqa: E501 } split_md5s = { "train": "033d0c23e3a75e3fa79618b0e35fe1c7", "val": "3e3f8b3c344182b8d126c4cc88f3f215", "test": "f908f151b950f270ad18e61153579794", }

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