LandCover.ai

class torchgeo.datasets.LandCoverAI(root='data', split='train', transforms=None, download=False, checksum=False)

Bases: LandCoverAIBase, NonGeoDataset

LandCover.ai dataset.

See the abstract LandCoverAIBase class to find out more.

Note

This dataset uses a pre-chipped version of the data available on HuggingFace. The pre-chipped dataset contains the output/ directory with 512x512 image chips and corresponding masks in JPG/PNG format.

__init__(root='data', split='train', transforms=None, download=False, checksum=False)

Initialize a new LandCover.ai dataset instance.

Parameters:

• root (str | PathLike[str]) – root directory where dataset can be found

• split (Literal['train', 'val', 'test']) – one of “train”, “val”, or “test”

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

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

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

Raises:

• AssertionError – if split argument is invalid

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

__getitem__(index)

Return an index within the dataset.

Parameters:

index (int) – index to return

Returns:

data and label at that index

Return type:

dict[str, Any]

__len__()

Return the number of data points in the dataset.

Returns:

length of the dataset

Return type:

int

__annotate_func__()

The type of the None singleton.

class torchgeo.datasets.LandCoverAI100(root='data', split='train', transforms=None, download=False, checksum=False)

Bases: LandCoverAI

Subset of LandCoverAI containing only 100 images.

Intended for tutorials and demonstrations, not for benchmarking.

Maintains the same file structure, classes, and train-val-test split.

Added in version 0.7.