OSCD

class torchgeo.datasets.OSCD(root='data', split='train', bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12'), transforms=None, download=False, checksum=False)

Bases: NonGeoDataset

OSCD dataset.

The Onera Satellite Change Detection dataset addresses the issue of detecting changes between satellite images from different dates. Imagery comes from Sentinel-2 which contains varying resolutions per band.

Dataset format:

• images are 13-channel tifs

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

Dataset classes:

0. no change

1. change

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

• https://doi.org/10.1109/IGARSS.2018.8518015

Added in version 0.2.

__init__(root='data', split='train', bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12'), transforms=None, download=False, checksum=False)

Initialize a new OSCD dataset instance.

Parameters:

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

• split (str) – one of “train” or “test”

• bands (Sequence[str]) – bands to return (defaults to all bands)

• 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.

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__()

Return the number of data points in the dataset.

Returns:

length of the dataset

Return type:

int

plot(sample, show_titles=True, suptitle=None, alpha=0.5)

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

• alpha (float) – opacity with which to render predictions on top of the imagery

Returns:

a matplotlib Figure with the rendered sample

Raises:

RGBBandsMissingError – If bands does not include all RGB bands.

Return type:

Figure

__annotate_func__()

The type of the None singleton.

class torchgeo.datasets.OSCD100(root='data', split='train', bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B10', 'B11', 'B12'), transforms=None, download=False, checksum=False)

Bases: OSCD

Subset of OSCD with 100 pre-cropped image pairs at 256x256 resolution.

Intended for tutorials and demonstrations, not benchmarking.

Maintains the same file structure and all 13 Sentinel-2 bands as OSCD, but with 100 pre-cropped 256x256 patches. Adds a validation split (train/val/test).

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

• https://doi.org/10.1109/IGARSS.2018.8518015

Added in version 0.9.

__annotate_func__()

The type of the None singleton.