EU-DEM#

class torchgeo.datasets.EUDEM(paths='data', crs=None, res=None, transforms=None, cache=True, checksum=False, time_series=False)[source]#

Bases: RasterDataset

European Digital Elevation Model (EU-DEM) Dataset.

EU-DEM is a Digital Elevation Model of reference for the entire European region.

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

Added in version 0.3.

is_image = False#

True if the dataset only contains model inputs (such as images). False if the dataset only contains ground truth model outputs (such as segmentation masks).

The sample returned by the dataset/data loader will use the “image” key if is_image is True, otherwise it will use the “mask” key.

For datasets with both model inputs and outputs, the recommended approach is to use 2 RasterDataset instances and combine them using an IntersectionDataset.

filename_glob = 'eu_dem_v11_*.TIF'#

Glob expression used to search for files.

This expression should be specific enough that it will not pick up files from other datasets. It should not include a file extension, as the dataset may be in a different file format than what it was originally downloaded as.

filename_regex = '(?P<name>[eudem_v11]{10})_(?P<id>[A-Z0-9]{6})'#

Regular expression used to extract date from filename.

The expression should use named groups. The expression may contain any number of groups. The following groups are specifically searched for by the base class:

  • date: used to calculate mint and maxt for index insertion

  • start: used to calculate mint for index insertion

  • stop: used to calculate maxt for index insertion

When separate_files is True, the following additional groups are searched for to find other files:

  • band: replaced with requested band name

__init__(paths='data', crs=None, res=None, transforms=None, cache=True, checksum=False, time_series=False)[source]#

Initialize a new Dataset instance.

Parameters:

  • paths (str | PathLike[str] | Iterable[str | PathLike[str]]) – 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 (CRS | None) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)

  • res (float | tuple[float, float] | None) – resolution of the dataset in units of CRS in (xres, yres) format. If a single float is provided, it is used for both the x and y resolution. (defaults to the resolution of the first file found)

  • transforms (Callable[[dict[str, Any]], dict[str, Any]] | None) – a function/transform that takes an input sample and returns a transformed version

  • cache (bool) – if True, cache file handle to speed up repeated sampling

  • checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)

  • time_series (bool) – if True, stack data along the time series dimension [T, C, H, W]. If False, merge data into a [C, H, W] mosaic.

Raises:

DatasetNotFoundError – If dataset is not found.

Added in version 0.9: The time_series parameter.

Changed in version 0.5: root was renamed to paths.

plot(sample, show_titles=True, suptitle=None)[source]#

Plot a sample from the dataset.

Parameters:

  • sample (dict[str, Any]) – a sample returned by RasterDataset.__getitem__()

  • show_titles (bool) – flag indicating whether to show titles above each panel

  • suptitle (str | None) – optional string to use as a suptitle

Returns:

a matplotlib Figure with the rendered sample

Return type:

Figure

__annotate_func__()#

The type of the None singleton.