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Source code for torchgeo.datasets.so2sat

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

"""So2Sat dataset."""

import os
from collections.abc 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 NonGeoDataset
from .utils import check_integrity, percentile_normalization


[docs]class So2Sat(NonGeoDataset): """So2Sat dataset. The `So2Sat <https://doi.org/10.1109/MGRS.2020.2964708>`__ dataset consists of corresponding synthetic aperture radar and multispectral optical image data acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and a corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world, and comes with a variety of different splits. This implementation covers the *2nd* and *3rd* versions of the dataset as described in the author's github repository: https://github.com/zhu-xlab/So2Sat-LCZ42. The different versions are as follows: Version 2: This version contains imagery from 52 cities and is split into train/val/test as follows: * Training: 42 cities around the world * Validation: western half of 10 other cities covering 10 cultural zones * Testing: eastern half of the 10 other cities Version 3: A version of the dataset with 3 different train/test splits, as follows: * Random split: every city 80% training / 20% testing (randomly sampled) * Block split: every city is split in a geospatial 80%/20%-manner * Cultural 10: 10 cities from different cultural zones are held back for testing purposes Dataset classes: 0. Compact high rise 1. Compact middle rise 2. Compact low rise 3. Open high rise 4. Open mid rise 5. Open low rise 6. Lightweight low rise 7. Large low rise 8. Sparsely built 9. Heavy industry 10. Dense trees 11. Scattered trees 12. Bush, scrub 13. Low plants 14. Bare rock or paved 15. Bare soil or sand 16. Water If you use this dataset in your research, please cite the following paper: * https://doi.org/10.1109/MGRS.2020.2964708 .. note:: The version 2 dataset can be automatically downloaded using the following bash script: .. code-block:: bash for split in training validation testing do wget ftp://m1483140:m1483140@dataserv.ub.tum.de/$split.h5 done or manually downloaded from https://dataserv.ub.tum.de/index.php/s/m1483140 This download will likely take several hours. The version 3 datasets can be downloaded using the following bash script: .. code-block:: bash for version in random block culture_10 do for split in training testing do wget -P $version/ ftp://m1613658:m1613658@dataserv.ub.tum.de/$version/$split.h5 done done or manually downloaded from https://mediatum.ub.tum.de/1613658 """ # noqa: E501 versions = ["2", "3_random", "3_block", "3_culture_10"] filenames_by_version = { "2": { "train": "training.h5", "validation": "validation.h5", "test": "testing.h5", }, "3_random": {"train": "random/training.h5", "test": "random/testing.h5"}, "3_block": {"train": "block/training.h5", "test": "block/testing.h5"}, "3_culture_10": { "train": "culture_10/training.h5", "test": "culture_10/testing.h5", }, } md5s_by_version = { "2": { "train": "702bc6a9368ebff4542d791e53469244", "validation": "71cfa6795de3e22207229d06d6f8775d", "test": "e81426102b488623a723beab52b31a8a", }, "3_random": { "train": "94e2e2e667b406c2adf61e113b42204e", "test": "1e15c425585ce816342d1cd779d453d8", }, "3_block": { "train": "a91d6150e8b059dac86105853f377a11", "test": "6414af1ec33ace417e879f9c88066d47", }, "3_culture_10": { "train": "702bc6a9368ebff4542d791e53469244", "test": "58335ce34ca3a18424e19da84f2832fc", }, } classes = [ "Compact high rise", "Compact mid rise", "Compact low rise", "Open high rise", "Open mid rise", "Open low rise", "Lightweight low rise", "Large low rise", "Sparsely built", "Heavy industry", "Dense trees", "Scattered trees", "Bush, scrub", "Low plants", "Bare rock or paved", "Bare soil or sand", "Water", ] all_s1_band_names = ( "S1_B1", "S1_B2", "S1_B3", "S1_B4", "S1_B5", "S1_B6", "S1_B7", "S1_B8", ) all_s2_band_names = ( "S2_B02", "S2_B03", "S2_B04", "S2_B05", "S2_B06", "S2_B07", "S2_B08", "S2_B8A", "S2_B11", "S2_B12", ) all_band_names = all_s1_band_names + all_s2_band_names rgb_bands = ["S2_B04", "S2_B03", "S2_B02"] BAND_SETS = { "all": all_band_names, "s1": all_s1_band_names, "s2": all_s2_band_names, "rgb": rgb_bands, }
[docs] def __init__( self, root: str = "data", version: str = "2", split: str = "train", bands: Sequence[str] = BAND_SETS["all"], transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None, checksum: bool = False, ) -> None: """Initialize a new So2Sat dataset instance. Args: root: root directory where dataset can be found version: one of "2", "3_random", "3_block", or "3_culture_10" split: one of "train", "validation", or "test" bands: a sequence of band names to use where the indices correspond to the array index of combined Sentinel 1 and Sentinel 2 transforms: a function/transform that takes input sample and its target as entry and returns a transformed version checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: AssertionError: if ``split`` argument is invalid RuntimeError: if data is not found in ``root``, or checksums don't match .. versionadded:: 0.3 The *bands* parameter. .. versionadded:: 0.5 The *version* parameter. """ try: import h5py # noqa: F401 except ImportError: raise ImportError( "h5py is not installed and is required to use this dataset" ) assert version in self.versions assert split in self.filenames_by_version[version] self._validate_bands(bands) self.s1_band_indices: "np.typing.NDArray[np.int_]" = np.array( [ self.all_s1_band_names.index(b) for b in bands if b in self.all_s1_band_names ] ).astype(int) self.s1_band_names = [self.all_s1_band_names[i] for i in self.s1_band_indices] self.s2_band_indices: "np.typing.NDArray[np.int_]" = np.array( [ self.all_s2_band_names.index(b) for b in bands if b in self.all_s2_band_names ] ).astype(int) self.s2_band_names = [self.all_s2_band_names[i] for i in self.s2_band_indices] self.bands = bands self.root = root self.version = version self.split = split self.transforms = transforms self.checksum = checksum self.fn = os.path.join(self.root, self.filenames_by_version[version][split]) if not self._check_integrity(): raise RuntimeError("Dataset not found or corrupted.") with h5py.File(self.fn, "r") as f: self.size: int = f["label"].shape[0]
[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 """ import h5py with h5py.File(self.fn, "r") as f: s1 = f["sen1"][index].astype(np.float64) # convert from <f8 to float64 s1 = np.take(s1, indices=self.s1_band_indices, axis=2) s2 = f["sen2"][index].astype(np.float64) # convert from <f8 to float64 s2 = np.take(s2, indices=self.s2_band_indices, axis=2) # convert one-hot encoding to int64 then torch int label = torch.tensor(f["label"][index].argmax()) s1 = np.rollaxis(s1, 2, 0) # convert to CxHxW format s2 = np.rollaxis(s2, 2, 0) # convert to CxHxW format s1 = torch.from_numpy(s1) s2 = torch.from_numpy(s2) sample = {"image": torch.cat([s1, s2]).float(), "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 self.size
def _check_integrity(self) -> bool: """Check integrity of dataset. Returns: True if dataset files are found and/or MD5s match, else False """ md5 = self.md5s_by_version[self.version][self.split] if not check_integrity(self.fn, md5 if self.checksum else None): return False return True 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:`__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.s2_band_names: idx = self.s2_band_names.index(band) + len(self.s1_band_names) rgb_indices.append(idx) 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 = percentile_normalization(image, 0, 100) 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

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