Digital Typhoon#

class torchgeo.datasets.DigitalTyphoon(root='data', task='regression', features=['wind'], targets=['wind'], sequence_length=3, min_feature_value=None, max_feature_value=None, transforms=None, download=False, checksum=False)[source]#

Bases: NonGeoDataset

Digital Typhoon Dataset for Analysis Task.

This dataset contains typhoon-centered images, derived from hourly infrared channel images captured by meteorological satellites. It incorporates data from multiple generations of the Himawari weather satellite, dating back to 1978. These images have been transformed into brightness temperatures and adjusted for varying satellite sensor readings, yielding a consistent spatio-temporal dataset that covers over four decades.

See the Digital Typhoon website for more information about the dataset.

Dataset features:

  • infrared channel images from the Himawari weather satellite (512x512 px) at 5km spatial resolution

  • auxiliary features such as wind speed, pressure, and more that can be used for regression or classification tasks

  • 1,099 typhoons and 189,364 images

Dataset format:

  • hdf5 files containing the infrared channel images

  • .csv files containing the metadata for each image

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

Added in version 0.6.

__init__(root='data', task='regression', features=['wind'], targets=['wind'], sequence_length=3, min_feature_value=None, max_feature_value=None, transforms=None, download=False, checksum=False)[source]#

Initialize a new Digital Typhoon dataset instance.

Parameters:

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

  • task (str) – whether to load ‘regression’ or ‘classification’ labels

  • features (Sequence[str]) – which auxiliary features to return

  • targets (Sequence[str]) – which auxiliary features to use as targets

  • sequence_length (int) – length of the sequence to return

  • min_feature_value (dict[str, float] | None) – minimum value for each feature

  • max_feature_value (dict[str, float] | None) – maximum value for each feature

  • 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.

Parameters:

index (int) – index to return

Returns:

data, labels, and metadata 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

__annotate_func__()#

The type of the None singleton.

plot(sample, show_titles=True, suptitle=None)[source]#

Plot a sample from the dataset.

Parameters:

  • sample (dict[str, Any]) – a sample return 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