BRIGHT#

class torchgeo.datasets.BRIGHTDFC2025(root='data', split='train', transforms=None, download=False, checksum=False)[source]#

Bases: NonGeoDataset

BRIGHT DFC2025 dataset.

The BRIGHT dataset consists of bi-temporal high-resolution multimodal images for building damage assessment. The dataset is part of the 2025 IEEE GRSS Data Fusion Contest. The pre-disaster images are optical images and the post-disaster images are SAR images, and targets were manually annotated. The dataset is split into train, val, and test splits, but the test split does not contain targets in this version.

More information can be found at the Challenge website.

Dataset Features:

  • Pre-disaster optical images from MAXAR, NAIP, NOAA Digital Coast Raster Datasets, and the National Plan for Aerial Orthophotography Spain

  • Post-disaster SAR images from Capella Space and Umbra

  • high image resolution of 0.3-1m

Dataset Format:

  • Images are in GeoTIFF format with pixel dimensions of 1024x1024

  • Pre-disaster are three channel images

  • Post-disaster SAR images are single channel but repeated to have 3 channels

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

Added in version 0.7.

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

Initialize a new BRIGHT DFC2025 dataset instance.

Parameters:

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

  • split (str) – train/val/test split to load

  • 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 target at that index, pre and post image are returned under separate image keys

Return type:

dict[str, Any]

__len__()[source]#

Return the number of samples in the dataset.

Returns:

number of samples in 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 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.