Source code for torchgeo.datasets.eudem

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

"""European Digital Elevation Model (EU-DEM) dataset."""

import glob
import os
from import Iterable
from typing import Any, Callable, Optional, Union

import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from import CRS

from .geo import RasterDataset
from .utils import check_integrity, extract_archive

[docs]class EUDEM(RasterDataset): """European Digital Elevation Model (EU-DEM) Dataset. The `EU-DEM <>`__ dataset is a Digital Elevation Model of reference for the entire European region. The dataset can be downloaded from this `website <>`_ after making an account. A dataset factsheet is available `here <>`__. Dataset features: * DEMs at 25 m per pixel spatial resolution (~40,000x40,0000 px) * vertical accuracy of +/- 7 m RMSE * data fused from `ASTER GDEM <>`_, `SRTM <>`_ and Russian topomaps Dataset format: * DEMs are single-channel tif files If you use this dataset in your research, please give credit to: * `Copernicus <>`_ .. versionadded:: 0.3 """ is_image = False filename_glob = "eu_dem_v11_*.TIF" zipfile_glob = "eu_dem_v11_*[A-Z0-9].zip" filename_regex = "(?P<name>[eudem_v11]{10})_(?P<id>[A-Z0-9]{6})" md5s = { "": "96edc7e11bc299b994e848050d6be591", "": "e14be147ac83eddf655f4833d55c1571", "": "2eb5187e4d827245b33768404529c709", "": "1afc162eb131841aed0d00b692b870a8", "": "77b040791b9fb7de271b3f47130b4e0c", "": "89b965abdcb1dbd479c61117f55230c8", "": "f5cb1b05813ae8ffc9e70f0ad56cc372", "": "81be551ff646802d7d820385de7476e9", "": "bbc351713ea3eb7e9eb6794acb9e4bc8", "": "68fb95aac33a025c4f35571f32f237ff", "": "da8ad029f9cc1ec9234ea3e7629fe18d", "": "de27c78d0176e45aec5c9e462a95749c", "": "4c00e58b624adfc4a5748c922e77ee40", "": "4a21a88f4d2047b8995d1101df0b3a77", "": "32fdf4572581eddc305a21c5d2f4bc81", "": "71b027f29258493dd751cfd63f08578f", "": "c6c21289882c1f74fc4649d255302c64", "": "9f26e6e47f4160ef8ea5200e8cf90a45", "": "a8c3c1c026cdd1537b8a3822c15834d9", "": "9584273c7708b8e935f2bac3e30c19c6", "": "8efdea43e7b6819861935d5a768a55f2", "": "e39e58df1c13ac35eb0b29fb651f313c", "": "d84395ab52ad254d930db17398fffc50", "": "6abe852f4a20962db0e355ffc0d695a4", "": "b6a3b8a39a4efc01c7e2cd8418672559", "": "71dc3c55ab5c90628ce2149dbd60f090", "": "5342465ad60cf7d28a586c9585179c35", }
[docs] def __init__( self, paths: Union[str, Iterable[str]] = "data", crs: Optional[CRS] = None, res: Optional[float] = None, transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None, cache: bool = True, checksum: bool = False, ) -> None: """Initialize a new Dataset instance. Args: paths: one or more root directories to search or files to load, here the collection of individual zip files for each tile should be found 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 checksum: if True, check the MD5 of the downloaded files (may be slow) Raises: FileNotFoundError: if no files are found in ``paths`` .. versionchanged:: 0.5 *root* was renamed to *paths*. """ self.paths = paths self.checksum = checksum self._verify() super().__init__(paths, crs, res, transforms=transforms, cache=cache)
def _verify(self) -> None: """Verify the integrity of the dataset. Raises: RuntimeError: if dataset is missing or checksum fails """ # Check if the extracted file already exists if self.files: return # Check if the zip files have already been downloaded assert isinstance(self.paths, str) pathname = os.path.join(self.paths, self.zipfile_glob) if glob.glob(pathname): for zipfile in glob.iglob(pathname): filename = os.path.basename(zipfile) if self.checksum and not check_integrity(zipfile, self.md5s[filename]): raise RuntimeError("Dataset found, but corrupted.") extract_archive(zipfile) return raise RuntimeError( f"Dataset not found in `paths={self.paths!r}` " "either specify a different `paths` or make sure you " "have manually downloaded the dataset as suggested in the documentation." )
[docs] 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:`RasterDataset.__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 showing_predictions = "prediction" in sample if showing_predictions: pred = sample["prediction"].squeeze() ncols = 2 fig, axs = plt.subplots(nrows=1, ncols=ncols, figsize=(ncols * 4, 4)) if showing_predictions: axs[0].imshow(mask) axs[0].axis("off") axs[1].imshow(pred) axs[1].axis("off") if show_titles: axs[0].set_title("Mask") axs[1].set_title("Prediction") else: axs.imshow(mask) axs.axis("off") if show_titles: axs.set_title("Mask") if suptitle is not None: plt.suptitle(suptitle) return fig

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