GlobBiomass#

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

Bases: RasterDataset

GlobBiomass dataset.

The GlobBiomass dataset consists of global pixelwise aboveground biomass (AGB) and growth stock volume (GSV) maps.

Definitions:

  • AGB: the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots.

  • GSV: volume of all living trees more than 10 cm in diameter at breast height measured over bark from ground or stump height to a top stem diameter of 0 cm.

Units:

  • AGB: m3/ha

  • GSV: tons/ha (i.e., Mg/ha)

Dataset features:

  • Global estimates of AGB and GSV at ~100 m per pixel resolution (45,000 x 45,000 px)

  • Per-pixel uncertainty expressed as standard error

Dataset format:

  • Estimate maps are single-channel

  • Uncertainty maps are single-channel

The data can be manually downloaded from this website.

If you use this dataset in your research, please cite the following dataset:

Added in version 0.3.

filename_regex = '\n        ^(?P<tile>[NS][\\d]{2}[EW][\\d]{3})\n        _(?P<measurement>(agb|gsv))\n    '#

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

mint: datetime = Timestamp('2010-01-01 00:00:00')#

Minimum timestamp if not in filename

maxt: datetime = Timestamp('2010-12-31 23:59:59.999999')#

Maximum timestamp if not in filename

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.

dtype = torch.float32#
__init__(paths='data', crs=None, res=None, measurement='agb', transforms=None, cache=True, checksum=False, time_series=False)[source]#

Initialize a new GlobBiomass instance.

Parameters:

  • paths (str | PathLike[str] | Iterable[str | PathLike[str]]) – one or more root directories to search or files to load

  • 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)

  • measurement (str) – use data from ‘agb’ or ‘gsv’ measurement

  • 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:

Added in version 0.9: The time_series parameter.

Changed in version 0.5: root was renamed to paths.

filename_glob = '*_{}.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.

__annotate_func__()#

The type of the None singleton.

__getitem__(index)[source]#

Retrieve input, target, and/or metadata indexed by spatiotemporal slice.

Parameters:

index (slice | tuple[slice] | tuple[slice, slice] | tuple[slice, slice, slice]) – [xmin:xmax:xres, ymin:ymax:yres, tmin:tmax:tres] coordinates to index.

Returns:

Sample of input, target, and/or metadata at that index.

Raises:

IndexError – If index is not found in the dataset.

Return type:

dict[str, Any]

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

Plot a sample from the dataset.

Parameters:

  • sample (dict[str, Any]) – a sample returned by __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