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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 typing import Callable, Dict, Optional, Sequence, cast

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

from .geo import NonGeoDataset
from .utils import check_integrity, percentile_normalization


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 split into fully independent, non-overlapping training, validation,
    and test sets.

    This implementation focuses on the *2nd* version of the dataset as described in
    the author's github repository https://github.com/zhu-xlab/So2Sat-LCZ42 and hosted
    at https://mediatum.ub.tum.de/1483140. This version is identical to the first
    version of the dataset but includes the test data. The splits are defined 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

    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::

       This 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.
    """

    filenames = {
        "train": "training.h5",
        "validation": "validation.h5",
        "test": "testing.h5",
    }
    md5s = {
        "train": "702bc6a9368ebff4542d791e53469244",
        "validation": "71cfa6795de3e22207229d06d6f8775d",
        "test": "e81426102b488623a723beab52b31a8a",
    }
    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 = ("S1B1", "S1B2", "S1B3", "S1B4", "S1B5", "S1B6", "S1B7", "S1B8")
    all_s2_band_names = (
        "B02",
        "B03",
        "B04",
        "B05",
        "B06",
        "B07",
        "B08",
        "B08A",
        "B11 SWIR",
        "B12 SWIR",
    )
    all_band_names = all_s1_band_names + all_s2_band_names

    RGB_BANDS = ["B04", "B03", "B02"]

    BAND_SETS = {
        "all": all_band_names,
        "s1": all_s1_band_names,
        "s2": all_s2_band_names,
    }

[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, checksum: bool = False, ) -> None: """Initialize a new So2Sat dataset instance. Args: root: root directory where dataset can be found 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. """ try: import h5py # noqa: F401 except ImportError: raise ImportError( "h5py is not installed and is required to use this dataset" ) assert split in ["train", "validation", "test"] 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.split = split self.transforms = transforms self.checksum = checksum self.fn = os.path.join(self.root, self.filenames[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]), "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[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, ) -> 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 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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