Kenya Crop Type#

class torchgeo.datasets.CV4AKenyaCropType(root='data', chip_size=256, stride=128, bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'CLD'), transforms=None, download=False)[source]#

Bases: NonGeoDataset

CV4A Kenya Crop Type Competition dataset.

The CV4A Kenya Crop Type Competition dataset was produced as part of the Crop Type Detection competition at the Computer Vision for Agriculture (CV4A) Workshop at the ICLR 2020 conference. The objective of the competition was to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 satellites.

See the dataset documentation for details.

Consists of 4 tiles of Sentinel 2 imagery from 13 different points in time.

Each tile has:

  • 13 multi-band observations throughout the growing season. Each observation includes 12 bands from Sentinel-2 L2A product, and a cloud probability layer. The twelve bands are [B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, B12] (refer to Sentinel-2 documentation for more information about the bands). The cloud probability layer is a product of the Sentinel-2 atmospheric correction algorithm (Sen2Cor) and provides an estimated cloud probability (0-100%) per pixel. All of the bands are mapped to a common 10 m spatial resolution grid.

  • A raster layer indicating the crop ID for the fields in the training set.

  • A raster layer indicating field IDs for the fields (both training and test sets). Fields with a crop ID 0 are the test fields.

There are 3,286 fields in the train set and 1,402 fields in the test set.

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

Note

This dataset requires the following additional library to be installed:

  • azcopy: to download the dataset from Source Cooperative.

__init__(root='data', chip_size=256, stride=128, bands=('B01', 'B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B09', 'B11', 'B12', 'CLD'), transforms=None, download=False)[source]#

Initialize a new CV4A Kenya Crop Type Dataset instance.

Parameters:

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

  • chip_size (int) – size of chips

  • stride (int) – spacing between chips, if less than chip_size, then there will be overlap between chips

  • bands (Sequence[str]) – the subset of bands 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

Raises:
__getitem__(index)[source]#

Return an index within the dataset.

Parameters:

index (int) – index to return

Returns:

data, labels, field ids, and metadata at that index

Return type:

dict[str, Any]

__len__()[source]#

Return the number of chips in the dataset.

Returns:

length of the dataset

Return type:

int

plot(sample, show_titles=True, time_step=0, 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

  • time_step (int) – time step at which to access image, beginning with 0

  • suptitle (str | None) – optional suptitle to use for figure

Returns:

a matplotlib Figure with the rendered sample

Raises:

RGBBandsMissingError – If bands does not include all RGB bands.

Return type:

Figure

Added in version 0.2.