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| # Copyright (c) Microsoft Corporation. All rights reserved. | ||
| # Licensed under the MIT License. | ||
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| """Northeastern China Crop Map Dataset.""" | ||
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| import glob | ||
| import os | ||
| from collections.abc import Iterable | ||
| import pathlib | ||
| from typing import Any, Callable, Optional, Union | ||
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| import matplotlib.pyplot as plt | ||
| import torch | ||
| from matplotlib.figure import Figure | ||
| from rasterio.crs import CRS | ||
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| from .geo import RasterDataset | ||
| from .utils import BoundingBox, DatasetNotFoundError, download_url, extract_archive | ||
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| class NCCM(RasterDataset): | ||
| """The Northeastern China Crop Map Dataset. | ||
| Link: https://www.nature.com/articles/s41597-021-00827-9 | ||
| This dataset produced annual 10-m crop maps of the | ||
| major crops (maize, soybean, and rice) | ||
| in Northeast China from 2017 to 2019, using hierarchial mapping strategies, | ||
| random forest classifiers, interpolated and | ||
| smoothed 10-day Sentinel-2 time series data and | ||
| optimized features from spectral, temporal and | ||
| textural characteristics of the land surface. | ||
| The resultant maps have high overall accuracies (OA) | ||
| based on ground truth data. The dataset contains information | ||
| specific to three years: 2017, 2018, 2019. | ||
| The dataset contains 5 classes: | ||
| 0. paddy rice | ||
| 1. maize | ||
| 2. soybean | ||
| 3. others crops and lands | ||
| 4. nodata | ||
| Dataset format: | ||
| * Three .TIF files containing the labels | ||
| * JavaScript code to download images from the dataset. | ||
| If you use this dataset in your research, please cite the following paper: | ||
| * https://doi.org/10.1038/s41597-021-00827-9 | ||
| .. versionadded:: 0.6 | ||
| """ | ||
|
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| filename_regex = r"CDL(?P<year>\d{4})_clip" | ||
| filename_glob = "CDL*.*" | ||
| zipfile_glob = "13090442.zip" | ||
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| date_format = "%Y" | ||
| is_image = False | ||
| url = "https://figshare.com/ndownloader/articles/13090442/versions/1" | ||
| md5 = "eae952f1b346d7e649d027e8139a76f5" | ||
|
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| cmap = { | ||
| 0: (0, 255, 0, 255), | ||
| 1: (255, 0, 0, 255), | ||
| 2: (255, 255, 0, 255), | ||
| 3: (128, 128, 128, 255), | ||
| 15: (255, 255, 255, 255), | ||
| } | ||
|
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| def __init__( | ||
| self, | ||
| paths: Union[pathlib.Path, str, Iterable[Union[pathlib.Path, str]]] = "data", | ||
| crs: Optional[CRS] = None, | ||
| res: Optional[float] = None, | ||
| transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None, | ||
| cache: bool = True, | ||
| download: bool = False, | ||
| checksum: bool = False, | ||
| ) -> None: | ||
| """Initialize a new dataset. | ||
| Args: | ||
| paths: one or more root directories to search or files to load | ||
| crs: :term:`coordinate reference system (CRS)` to warp to | ||
| (defaults to the CRS of the first file found) | ||
| res: resolution of the dataset in units of CRS | ||
| (defaults to the resolution of the first file found) | ||
| transforms: a function/transform that takes an input sample | ||
| and returns a transformed version | ||
| cache: if True, cache file handle to speed up repeated sampling | ||
| download: if True, download dataset and store it in the root directory | ||
| checksum: if True, check the MD5 after downloading files (may be slow) | ||
| Raises: | ||
| DatasetNotFoundError: If dataset is not found and *download* is False. | ||
| """ | ||
| self.paths = paths | ||
| self.download = download | ||
| self.checksum = checksum | ||
| self.ordinal_map = torch.full((max(self.cmap.keys()) + 1,), 4, dtype=self.dtype) | ||
| self.ordinal_cmap = torch.zeros((5, 4), dtype=torch.uint8) | ||
|
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| self._verify() | ||
| super().__init__(paths, crs, res, transforms=transforms, cache=cache) | ||
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| for i, (k, v) in enumerate(self.cmap.items()): | ||
| self.ordinal_map[k] = i | ||
| self.ordinal_cmap[i] = torch.tensor(v) | ||
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| def __getitem__(self, query: BoundingBox) -> dict[str, Any]: | ||
| """Retrieve mask and metadata indexed by query. | ||
| Args: | ||
| query: (minx, maxx, miny, maxy, mint, maxt) coordinates to index | ||
| Returns: | ||
| sample of mask and metadata at that index | ||
| Raises: | ||
| IndexError: if query is not found in the index | ||
| """ | ||
| sample = super().__getitem__(query) | ||
| sample["mask"] = self.ordinal_map[sample["mask"]] | ||
| return sample | ||
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| def _verify(self) -> None: | ||
| """Verify the integrity of the dataset.""" | ||
| # Check if the extracted files already exist | ||
| if self.files: | ||
| return | ||
|
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| # Check if the zip file has already been downloaded | ||
| assert isinstance(self.paths, (pathlib.Path, str)) | ||
| pathname = os.path.join(self.paths, "**", self.zipfile_glob) | ||
| if glob.glob(pathname, recursive=True): | ||
| self._extract() | ||
| return | ||
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| # Check if the user requested to download the dataset | ||
| if not self.download: | ||
| raise DatasetNotFoundError(self) | ||
|
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| # Download the dataset | ||
| self._download() | ||
| self._extract() | ||
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| def _download(self) -> None: | ||
| """Download the dataset.""" | ||
| filename = "13090442.zip" | ||
| download_url( | ||
| self.url, self.paths, filename, md5=self.md5 if self.checksum else None | ||
| ) | ||
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| def _extract(self) -> None: | ||
| """Extract the dataset.""" | ||
| assert isinstance(self.paths, (pathlib.Path, str)) | ||
| pathname = os.path.join(self.paths, "**", self.zipfile_glob) | ||
| extract_archive(glob.glob(pathname, recursive=True)[0], self.paths) | ||
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| def plot( | ||
| self, | ||
| sample: dict[str, Any], | ||
| show_titles: bool = True, | ||
| suptitle: Optional[str] = None, | ||
| ) -> Figure: | ||
| """Plot a sample from the dataset. | ||
| Args: | ||
| sample: a sample returned by :meth:`NCCM.__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 | ||
| """ | ||
| mask = sample["mask"].squeeze() | ||
| ncols = 1 | ||
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| showing_predictions = "prediction" in sample | ||
| if showing_predictions: | ||
| pred = sample["prediction"].squeeze() | ||
| ncols = 2 | ||
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| fig, axs = plt.subplots( | ||
| nrows=1, ncols=ncols, figsize=(ncols * 4, 4), squeeze=False | ||
| ) | ||
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| axs[0, 0].imshow(self.ordinal_cmap[mask], interpolation="none") | ||
| axs[0, 0].axis("off") | ||
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| if show_titles: | ||
| axs[0, 0].set_title("Mask") | ||
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| if showing_predictions: | ||
| axs[0, 1].imshow(self.ordinal_cmap[pred], interpolation="none") | ||
| axs[0, 1].axis("off") | ||
| if show_titles: | ||
| axs[0, 1].set_title("Prediction") | ||
|
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| if suptitle is not None: | ||
| plt.suptitle(suptitle) | ||
|
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| return fig |
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