LEVIR-CD#

class torchgeo.datasets.LEVIRCDBase(root='data', split='train', transforms=None, download=False, checksum=False)[source]#

Bases: NonGeoDataset, ABC

Abstract base class for the LEVIRCD datasets.

Added in version 0.6.

__init__(root='data', split='train', transforms=None, download=False, checksum=False)[source]#

Initialize a new LEVIR-CD base dataset instance.

Parameters:

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

  • split (str) – one of “train” 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:
__getitem__(index)[source]#

Return an index within the dataset.

Changed in version 0.8: Now returns a single T x C x H x W image.

Parameters:

index (int) – index to return

Returns:

data and label at that index

Return type:

dict[str, Any]

__len__()[source]#

Return the number of data points in the dataset.

Returns:

length of the dataset

Return type:

int

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 suptitle to use for figure

Returns:

a matplotlib Figure with the rendered sample

Return type:

Figure

Added in version 0.2.

__annotate_func__()#

The type of the None singleton.

class torchgeo.datasets.LEVIRCD(root='data', split='train', transforms=None, download=False, checksum=False)[source]#

Bases: LEVIRCDBase

LEVIR-CD dataset.

The LEVIR-CD dataset is a dataset for building change detection.

Dataset features:

  • image pairs of 20 different urban regions across Texas between 2002-2018

  • binary change masks representing building change

  • three spectral bands - RGB

  • 637 image pairs with 50 cm per pixel resolution (~1024x1024 px)

Dataset format:

  • images are three-channel pngs

  • masks are single-channel pngs where no change = 0, change = 255

Dataset classes:

  1. no change

  2. change

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

Added in version 0.6.

__annotate_func__()#

The type of the None singleton.