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Source code for torchgeo.datasets.bigearthnet

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

"""BigEarthNet dataset."""

import glob
import json
import os
from typing import Callable, Dict, List, Optional

import matplotlib.pyplot as plt
import numpy as np
import rasterio
import torch
from rasterio.enums import Resampling
from torch import Tensor

from .geo import NonGeoDataset
from .utils import download_url, extract_archive, sort_sentinel2_bands


class BigEarthNet(NonGeoDataset):
    """BigEarthNet dataset.

    The `BigEarthNet <https://bigearth.net/>`__
    dataset is a dataset for multilabel remote sensing image scene classification.

    Dataset features:

    * 590,326 patches from 125 Sentinel-1 and Sentinel-2 tiles
    * Imagery from tiles in Europe between Jun 2017 - May 2018
    * 12 spectral bands with 10-60 m per pixel resolution (base 120x120 px)
    * 2 synthetic aperture radar bands (120x120 px)
    * 43 or 19 scene classes from the 2018 CORINE Land Cover database (CLC 2018)

    Dataset format:

    * images are composed of multiple single channel geotiffs
    * labels are multiclass, stored in a single json file per image
    * mapping of Sentinel-1 to Sentinel-2 patches are within Sentinel-1 json files
    * Sentinel-1 bands: (VV, VH)
    * Sentinel-2 bands: (B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
    * All bands: (VV, VH, B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12)
    * Sentinel-2 bands are of different spatial resolutions and upsampled to 10m

    Dataset classes (43):

    0. Agro-forestry areas
    1. Airports
    2. Annual crops associated with permanent crops
    3. Bare rock
    4. Beaches, dunes, sands
    5. Broad-leaved forest
    6. Burnt areas
    7. Coastal lagoons
    8. Complex cultivation patterns
    9. Coniferous forest
    10. Construction sites
    11. Continuous urban fabric
    12. Discontinuous urban fabric
    13. Dump sites
    14. Estuaries
    15. Fruit trees and berry plantations
    16. Green urban areas
    17. Industrial or commercial units
    18. Inland marshes
    19. Intertidal flats
    20. Land principally occupied by agriculture, with significant
        areas of natural vegetation
    21. Mineral extraction sites
    22. Mixed forest
    23. Moors and heathland
    24. Natural grassland
    25. Non-irrigated arable land
    26. Olive groves
    27. Pastures
    28. Peatbogs
    29. Permanently irrigated land
    30. Port areas
    31. Rice fields
    32. Road and rail networks and associated land
    33. Salines
    34. Salt marshes
    35. Sclerophyllous vegetation
    36. Sea and ocean
    37. Sparsely vegetated areas
    38. Sport and leisure facilities
    39. Transitional woodland/shrub
    40. Vineyards
    41. Water bodies
    42. Water courses

    Dataset classes (19):

    0. Urban fabric
    1. Industrial or commercial units
    2. Arable land
    3. Permanent crops
    4. Pastures
    5. Complex cultivation patterns
    6. Land principally occupied by agriculture, with significant
       areas of natural vegetation
    7. Agro-forestry areas
    8. Broad-leaved forest
    9. Coniferous forest
    10. Mixed forest
    11. Natural grassland and sparsely vegetated areas
    12. Moors, heathland and sclerophyllous vegetation
    13. Transitional woodland, shrub
    14. Beaches, dunes, sands
    15. Inland wetlands
    16. Coastal wetlands
    17. Inland waters
    18. Marine waters

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

    * https://doi.org/10.1109/IGARSS.2019.8900532

    """

    class_sets = {
        19: [
            "Urban fabric",
            "Industrial or commercial units",
            "Arable land",
            "Permanent crops",
            "Pastures",
            "Complex cultivation patterns",
            "Land principally occupied by agriculture, with significant areas of"
            " natural vegetation",
            "Agro-forestry areas",
            "Broad-leaved forest",
            "Coniferous forest",
            "Mixed forest",
            "Natural grassland and sparsely vegetated areas",
            "Moors, heathland and sclerophyllous vegetation",
            "Transitional woodland, shrub",
            "Beaches, dunes, sands",
            "Inland wetlands",
            "Coastal wetlands",
            "Inland waters",
            "Marine waters",
        ],
        43: [
            "Agro-forestry areas",
            "Airports",
            "Annual crops associated with permanent crops",
            "Bare rock",
            "Beaches, dunes, sands",
            "Broad-leaved forest",
            "Burnt areas",
            "Coastal lagoons",
            "Complex cultivation patterns",
            "Coniferous forest",
            "Construction sites",
            "Continuous urban fabric",
            "Discontinuous urban fabric",
            "Dump sites",
            "Estuaries",
            "Fruit trees and berry plantations",
            "Green urban areas",
            "Industrial or commercial units",
            "Inland marshes",
            "Intertidal flats",
            "Land principally occupied by agriculture, with significant areas of"
            " natural vegetation",
            "Mineral extraction sites",
            "Mixed forest",
            "Moors and heathland",
            "Natural grassland",
            "Non-irrigated arable land",
            "Olive groves",
            "Pastures",
            "Peatbogs",
            "Permanently irrigated land",
            "Port areas",
            "Rice fields",
            "Road and rail networks and associated land",
            "Salines",
            "Salt marshes",
            "Sclerophyllous vegetation",
            "Sea and ocean",
            "Sparsely vegetated areas",
            "Sport and leisure facilities",
            "Transitional woodland/shrub",
            "Vineyards",
            "Water bodies",
            "Water courses",
        ],
    }

    label_converter = {
        0: 0,
        1: 0,
        2: 1,
        11: 2,
        12: 2,
        13: 2,
        14: 3,
        15: 3,
        16: 3,
        18: 3,
        17: 4,
        19: 5,
        20: 6,
        21: 7,
        22: 8,
        23: 9,
        24: 10,
        25: 11,
        31: 11,
        26: 12,
        27: 12,
        28: 13,
        29: 14,
        33: 15,
        34: 15,
        35: 16,
        36: 16,
        38: 17,
        39: 17,
        40: 18,
        41: 18,
        42: 18,
    }

    splits_metadata = {
        "train": {
            "url": "https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/master/splits/train.csv?inline=false",  # noqa: E501
            "filename": "bigearthnet-train.csv",
            "md5": "623e501b38ab7b12fe44f0083c00986d",
        },
        "val": {
            "url": "https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/master/splits/val.csv?inline=false",  # noqa: E501
            "filename": "bigearthnet-val.csv",
            "md5": "22efe8ed9cbd71fa10742ff7df2b7978",
        },
        "test": {
            "url": "https://git.tu-berlin.de/rsim/BigEarthNet-MM_19-classes_models/-/raw/master/splits/test.csv?inline=false",  # noqa: E501
            "filename": "bigearthnet-test.csv",
            "md5": "697fb90677e30571b9ac7699b7e5b432",
        },
    }
    metadata = {
        "s1": {
            "url": "https://bigearth.net/downloads/BigEarthNet-S1-v1.0.tar.gz",
            "md5": "94ced73440dea8c7b9645ee738c5a172",
            "filename": "BigEarthNet-S1-v1.0.tar.gz",
            "directory": "BigEarthNet-S1-v1.0",
        },
        "s2": {
            "url": "https://bigearth.net/downloads/BigEarthNet-S2-v1.0.tar.gz",
            "md5": "5a64e9ce38deb036a435a7b59494924c",
            "filename": "BigEarthNet-S2-v1.0.tar.gz",
            "directory": "BigEarthNet-v1.0",
        },
    }
    image_size = (120, 120)

[docs] def __init__( self, root: str = "data", split: str = "train", bands: str = "all", num_classes: int = 19, transforms: Optional[Callable[[Dict[str, Tensor]], Dict[str, Tensor]]] = None, download: bool = False, checksum: bool = False, ) -> None: """Initialize a new BigEarthNet dataset instance. Args: root: root directory where dataset can be found split: train/val/test split to load bands: load Sentinel-1 bands, Sentinel-2, or both. one of {s1, s2, all} num_classes: number of classes to load in target. one of {19, 43} 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) """ assert split in self.splits_metadata assert bands in ["s1", "s2", "all"] assert num_classes in [43, 19] self.root = root self.split = split self.bands = bands self.num_classes = num_classes self.transforms = transforms self.download = download self.checksum = checksum self.class2idx = {c: i for i, c in enumerate(self.class_sets[43])} self._verify() self.folders = self._load_folders()
[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 = self._load_image(index) label = self._load_target(index) sample: Dict[str, Tensor] = {"image": image, "label": label} if self.transforms is not None: sample = self.transforms(sample) return sample
[docs] def __len__(self) -> int: """Return the number of data points in the dataset. Returns: length of the dataset """ return len(self.folders)
def _load_folders(self) -> List[Dict[str, str]]: """Load folder paths. Returns: list of dicts of s1 and s2 folder paths """ filename = self.splits_metadata[self.split]["filename"] dir_s1 = self.metadata["s1"]["directory"] dir_s2 = self.metadata["s2"]["directory"] with open(os.path.join(self.root, filename)) as f: lines = f.read().strip().splitlines() pairs = [line.split(",") for line in lines] folders = [ { "s1": os.path.join(self.root, dir_s1, pair[1]), "s2": os.path.join(self.root, dir_s2, pair[0]), } for pair in pairs ] return folders def _load_paths(self, index: int) -> List[str]: """Load paths to band files. Args: index: index to return Returns: list of file paths """ if self.bands == "all": folder_s1 = self.folders[index]["s1"] folder_s2 = self.folders[index]["s2"] paths_s1 = glob.glob(os.path.join(folder_s1, "*.tif")) paths_s2 = glob.glob(os.path.join(folder_s2, "*.tif")) paths_s1 = sorted(paths_s1) paths_s2 = sorted(paths_s2, key=sort_sentinel2_bands) paths = paths_s1 + paths_s2 elif self.bands == "s1": folder = self.folders[index]["s1"] paths = glob.glob(os.path.join(folder, "*.tif")) paths = sorted(paths) else: folder = self.folders[index]["s2"] paths = glob.glob(os.path.join(folder, "*.tif")) paths = sorted(paths, key=sort_sentinel2_bands) return paths def _load_image(self, index: int) -> Tensor: """Load a single image. Args: index: index to return Returns: the raster image or target """ paths = self._load_paths(index) images = [] for path in paths: # Bands are of different spatial resolutions # Resample to (120, 120) with rasterio.open(path) as dataset: array = dataset.read( indexes=1, out_shape=self.image_size, out_dtype="int32", resampling=Resampling.bilinear, ) images.append(array) arrays: "np.typing.NDArray[np.int_]" = np.stack(images, axis=0) tensor = torch.from_numpy(arrays) return tensor def _load_target(self, index: int) -> Tensor: """Load the target mask for a single image. Args: index: index to return Returns: the target label """ if self.bands == "s2": folder = self.folders[index]["s2"] else: folder = self.folders[index]["s1"] path = glob.glob(os.path.join(folder, "*.json"))[0] with open(path) as f: labels = json.load(f)["labels"] # labels -> indices indices = [self.class2idx[label] for label in labels] # Map 43 to 19 class labels if self.num_classes == 19: indices_optional = [self.label_converter.get(idx) for idx in indices] indices = [idx for idx in indices_optional if idx is not None] target = torch.zeros(self.num_classes, dtype=torch.long) target[indices] = 1 return target def _verify(self) -> None: """Verify the integrity of the dataset. Raises: RuntimeError: if ``download=False`` but dataset is missing or checksum fails """ keys = ["s1", "s2"] if self.bands == "all" else [self.bands] urls = [self.metadata[k]["url"] for k in keys] md5s = [self.metadata[k]["md5"] for k in keys] filenames = [self.metadata[k]["filename"] for k in keys] directories = [self.metadata[k]["directory"] for k in keys] urls.extend([self.splits_metadata[k]["url"] for k in self.splits_metadata]) md5s.extend([self.splits_metadata[k]["md5"] for k in self.splits_metadata]) filenames_splits = [ self.splits_metadata[k]["filename"] for k in self.splits_metadata ] filenames.extend(filenames_splits) # Check if the split file already exist exists = [] for filename in filenames_splits: exists.append(os.path.exists(os.path.join(self.root, filename))) # Check if the files already exist for directory in directories: exists.append(os.path.exists(os.path.join(self.root, directory))) if all(exists): return # Check if zip file already exists (if so then extract) exists = [] for filename in filenames: filepath = os.path.join(self.root, filename) if os.path.exists(filepath): exists.append(True) self._extract(filepath) else: exists.append(False) if all(exists): 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 automatically download the dataset." ) # Download and extract the dataset for url, filename, md5 in zip(urls, filenames, md5s): self._download(url, filename, md5) filepath = os.path.join(self.root, filename) self._extract(filepath) def _download(self, url: str, filename: str, md5: str) -> None: """Download the dataset. Args: url: url to download file filename: output filename to write downloaded file md5: md5 of downloaded file """ if not os.path.exists(filename): download_url( url, self.root, filename=filename, md5=md5 if self.checksum else None ) def _extract(self, filepath: str) -> None: """Extract the dataset. Args: filepath: path to file to be extracted """ if not filepath.endswith(".csv"): extract_archive(filepath) def _onehot_labels_to_names( self, label_mask: "np.typing.NDArray[np.bool_]" ) -> List[str]: """Gets a list of class names given a label mask. Args: label_mask: a boolean mask corresponding to a set of labels or predictions Returns a list of class names corresponding to the input mask """ labels = [] for i, mask in enumerate(label_mask): if mask: labels.append(self.class_sets[self.num_classes][i]) return labels
[docs] def plot( self, sample: Dict[str, Tensor], show_titles: bool = True, suptitle: Optional[str] = None, ) -> plt.Figure: """Plot a sample from the dataset. Args: sample: a sample returned by :meth:`__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 ``self.bands`` is "s1" .. versionadded:: 0.2 """ if self.bands == "s2": image = np.rollaxis(sample["image"][[3, 2, 1]].numpy(), 0, 3) image = np.clip(image / 2000, 0, 1) elif self.bands == "all": image = np.rollaxis(sample["image"][[5, 4, 3]].numpy(), 0, 3) image = np.clip(image / 2000, 0, 1) elif self.bands == "s1": image = sample["image"][0].numpy() label_mask = sample["label"].numpy().astype(np.bool_) labels = self._onehot_labels_to_names(label_mask) showing_predictions = "prediction" in sample if showing_predictions: prediction_mask = sample["prediction"].numpy().astype(np.bool_) predictions = self._onehot_labels_to_names(prediction_mask) fig, ax = plt.subplots(figsize=(4, 4)) ax.imshow(image) ax.axis("off") if show_titles: title = f"Labels: {', '.join(labels)}" if showing_predictions: title += f"\nPredictions: {', '.join(predictions)}" ax.set_title(title) if suptitle is not None: plt.suptitle(suptitle) return fig

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