NLCD

class torchgeo.datasets.NLCD(paths='data', crs=None, res=None, years=[2024], classes=[0, 11, 12, 21, 22, 23, 24, 31, 41, 42, 43, 52, 71, 81, 82, 90, 95], transforms=None, cache=True, download=False, checksum=False, time_series=False)

Bases: RasterDataset

Annual National Land Cover Database (NLCD) dataset.

The Annual NLCD products is an annual land cover product for the conterminous U.S. covering the period from 1985 to 2024. The product is a joint effort between the United States Geological Survey (USGS) and the Multi-Resolution Land Characteristics Consortium (MRLC).

The dataset contains the following 17 classes:

0. Background

1. Open Water

2. Perennial Ice/Snow

3. Developed, Open Space

4. Developed, Low Intensity

5. Developed, Medium Intensity

6. Developed, High Intensity

7. Barren Land (Rock/Sand/Clay)

8. Deciduous Forest

9. Evergreen Forest

10. Mixed Forest

11. Shrub/Scrub

12. Grassland/Herbaceous

13. Pasture/Hay

14. Cultivated Crops

15. Woody Wetlands

16. Emergent Herbaceous Wetlands

Detailed descriptions of the classes can be found here.

Dataset format:

• single channel .img file with integer class labels

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

• https://doi.org/10.5066/P94UXNTS

Added in version 0.5.

filename_glob = 'Annual_NLCD_LndCov_*_CU_C1V1.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 = 'Annual_NLCD_LndCov_(?P<date>\\d{4})_CU_C1V1\\.tif'

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

date_format = '%Y'

Date format string used to parse date from filename.

Not used if filename_regex does not contain a date group or start and stop groups.

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.

cmap: ClassVar[dict[int, tuple[int, int, int, int]]] = {0: (0, 0, 0, 0), 11: (70, 107, 159, 255), 12: (209, 222, 248, 255), 21: (222, 197, 197, 255), 22: (217, 146, 130, 255), 23: (235, 0, 0, 255), 24: (171, 0, 0, 255), 31: (179, 172, 159, 255), 41: (104, 171, 95, 255), 42: (28, 95, 44, 255), 43: (181, 197, 143, 255), 52: (204, 184, 121, 255), 71: (223, 223, 194, 255), 81: (220, 217, 57, 255), 82: (171, 108, 40, 255), 90: (184, 217, 235, 255), 95: (108, 159, 184, 255)}

Color map for the dataset, used for plotting

__init__(paths='data', crs=None, res=None, years=[2024], classes=[0, 11, 12, 21, 22, 23, 24, 31, 41, 42, 43, 52, 71, 81, 82, 90, 95], transforms=None, cache=True, download=False, checksum=False, time_series=False)

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

• 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)

• years (list[int]) – list of years for which to use nlcd layer

• classes (list[int]) – list of classes to include, the rest will be mapped to 0 (defaults to all classes)

• 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

• download (bool) – if True, download dataset and store it in the root directory

• checksum (bool) – if True, check the MD5 after downloading 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:

• AssertionError – if years or classes are invalid

• DatasetNotFoundError – If dataset is not found and download is False.

Added in version 0.9: The time_series parameter.

__getitem__(index)

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)

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

__annotate_func__()

The type of the None singleton.