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, Optional

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

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

[docs]class BigEarthNet(NonGeoDataset): """BigEarthNet dataset. The `BigEarthNet <>`__ 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. Continuous urban fabric 1. Discontinuous urban fabric 2. Industrial or commercial units 3. Road and rail networks and associated land 4. Port areas 5. Airports 6. Mineral extraction sites 7. Dump sites 8. Construction sites 9. Green urban areas 10. Sport and leisure facilities 11. Non-irrigated arable land 12. Permanently irrigated land 13. Rice fields 14. Vineyards 15. Fruit trees and berry plantations 16. Olive groves 17. Pastures 18. Annual crops associated with permanent crops 19. Complex cultivation patterns 20. Land principally occupied by agriculture, with significant areas of natural vegetation 21. Agro-forestry areas 22. Broad-leaved forest 23. Coniferous forest 24. Mixed forest 25. Natural grassland 26. Moors and heathland 27. Sclerophyllous vegetation 28. Transitional woodland/shrub 29. Beaches, dunes, sands 30. Bare rock 31. Sparsely vegetated areas 32. Burnt areas 33. Inland marshes 34. Peatbogs 35. Salt marshes 36. Salines 37. Intertidal flats 38. Water courses 39. Water bodies 40. Coastal lagoons 41. Estuaries 42. Sea and ocean 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 The source for the above dataset classes, their respective ordering, and 43-to-19-class mappings can be found here: * If you use this dataset in your research, please cite the following paper: * """ # noqa: E501 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: [ "Continuous urban fabric", "Discontinuous urban fabric", "Industrial or commercial units", "Road and rail networks and associated land", "Port areas", "Airports", "Mineral extraction sites", "Dump sites", "Construction sites", "Green urban areas", "Sport and leisure facilities", "Non-irrigated arable land", "Permanently irrigated land", "Rice fields", "Vineyards", "Fruit trees and berry plantations", "Olive groves", "Pastures", "Annual crops associated with permanent crops", "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", "Moors and heathland", "Sclerophyllous vegetation", "Transitional woodland/shrub", "Beaches, dunes, sands", "Bare rock", "Sparsely vegetated areas", "Burnt areas", "Inland marshes", "Peatbogs", "Salt marshes", "Salines", "Intertidal flats", "Water courses", "Water bodies", "Coastal lagoons", "Estuaries", "Sea and ocean", ], } 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": "", # noqa: E501 "filename": "bigearthnet-train.csv", "md5": "623e501b38ab7b12fe44f0083c00986d", }, "val": { "url": "", # noqa: E501 "filename": "bigearthnet-val.csv", "md5": "22efe8ed9cbd71fa10742ff7df2b7978", }, "test": { "url": "", # noqa: E501 "filename": "bigearthnet-test.csv", "md5": "697fb90677e30571b9ac7699b7e5b432", }, } metadata = { "s1": { "url": "", "md5": "94ced73440dea8c7b9645ee738c5a172", "filename": "BigEarthNet-S1-v1.0.tar.gz", "directory": "BigEarthNet-S1-v1.0", }, "s2": { "url": "", "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 = 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 = 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 as dataset: array = 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).float() 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 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, ) -> 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 .. 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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