FireRisk

class torchgeo.datasets.FireRisk(root='data', split='train', transforms=None, download=False, checksum=False)

Bases: NonGeoClassificationDataset

FireRisk dataset.

The FireRisk dataset is a dataset for remote sensing fire risk classification.

Dataset features:

• 91,872 images with 1 m per pixel resolution (320x320 px)

• 70,331 and 21,541 train and val images, respectively

• three spectral bands - RGB

• 7 fire risk classes

• images extracted from NAIP tiles

Dataset format:

• images are three-channel pngs

Dataset classes:

0. high

1. low

2. moderate

3. non-burnable

4. very_high

5. very_low

6. water

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

• https://arxiv.org/abs/2303.07035

Added in version 0.5.

__init__(root='data', split='train', transforms=None, download=False, checksum=False)

Initialize a new FireRisk dataset instance.

Parameters:

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

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

• 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.

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

Plot a sample from the dataset.

Parameters:

• sample (dict[str, Any]) – a sample returned by NonGeoClassificationDataset.__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