Source code for torchgeo.datasets.south_america_soybean
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""South America Soybean Dataset."""
from collections.abc import Callable, Iterable
from typing import Any
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from rasterio.crs import CRS
from .errors import DatasetNotFoundError
from .geo import RasterDataset
from .utils import download_url
[docs]class SouthAmericaSoybean(RasterDataset):
"""South America Soybean Dataset.
This dataset produced annual 30-m soybean maps of South America from 2001 to 2021.
Link: https://www.nature.com/articles/s41893-021-00729-z
Dataset contains 2 classes:
0. other
1. soybean
Dataset Format:
* 21 .tif files
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1038/s41893-021-00729-z
.. versionadded:: 0.6
"""
filename_glob = 'South_America_Soybean_*.*'
filename_regex = r'South_America_Soybean_(?P<year>\d{4})'
date_format = '%Y'
is_image = False
url = 'https://glad.umd.edu/projects/AnnualClassMapsV1/SouthAmerica_Soybean_{}.tif'
md5s = {
2021: 'edff3ada13a1a9910d1fe844d28ae4f',
2020: '0709dec807f576c9707c8c7e183db31',
2019: '441836493bbcd5e123cff579a58f5a4f',
2018: '503c2d0a803c2a2629ebbbd9558a3013',
2017: '4d0487ac1105d171e5f506f1766ea777',
2016: '770c558f6ac40550d0e264da5e44b3e',
2015: '6beb96a61fe0e9ce8c06263e500dde8f',
2014: '824ff91c62a4ba9f4ccfd281729830e5',
2013: '0263e19b3cae6fdaba4e3b450cef985e',
2012: '9f3a71097c9836fcff18a13b9ba608b2',
2011: 'b73352ebea3d5658959e9044ec526143',
2010: '9264532d36ffa93493735a6e44caef0d',
2009: '341387c1bb42a15140c80702e4cca02d',
2008: '96fc3f737ab3ce9bcd16cbf7761427e2',
2007: 'bb8549b6674163fe20ffd47ec4ce8903',
2006: 'eabaa525414ecbff89301d3d5c706f0b',
2005: '89faae27f9b5afbd06935a465e5fe414',
2004: 'f9882ca9c70e054e50172835cb75a8c3',
2003: 'cad5ed461ff4ab45c90177841aaecad2',
2002: '8a4a9dcea54b3ec7de07657b9f2c0893',
2001: '2914b0af7590a0ca4dfa9ccefc99020f',
}
[docs] def __init__(
self,
paths: str | Iterable[str] = 'data',
crs: CRS | None = None,
res: float | None = None,
years: list[int] = [2021],
transforms: Callable[[dict[str, Any]], dict[str, Any]] | None = None,
cache: bool = True,
download: bool = False,
checksum: bool = False,
) -> None:
"""Initialize a new Dataset instance.
Args:
paths: one or more root directories to search or files to load
crs: :term:`coordinate reference system (CRS)` to warp to
(defaults to the CRS of the first file found)
res: resolution of the dataset in units of CRS
(defaults to the resolution of the first file found)
years: list of years for which to use the South America Soybean layer
transforms: a function/transform that takes an input sample
and returns a transformed version
cache: if True, cache file handle to speed up repeated sampling
download: if True, download dataset and store it in the root directory
checksum: if True, check the MD5 after downloading files (may be slow)
Raises:
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
self.paths = paths
self.download = download
self.checksum = checksum
self.years = years
self._verify()
super().__init__(paths, crs, res, transforms=transforms, cache=cache)
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the extracted files already exist
if self.files:
return
assert isinstance(self.paths, str)
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
def _download(self) -> None:
"""Download the dataset."""
for year in self.years:
download_url(
self.url.format(year),
self.paths,
md5=self.md5s[year] if self.checksum else None,
)
[docs] def plot(
self,
sample: dict[str, Any],
show_titles: bool = True,
suptitle: str | None = None,
) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample returned by :meth:`RasterDataset.__getitem__`
show_titles: flag indicating whether to show titles above each panel
suptitle: optional string to use as a suptitle
Returns:
a matplotlib Figure with the rendered sample
"""
mask = sample['mask'].squeeze()
ncols = 1
showing_predictions = 'prediction' in sample
if showing_predictions:
pred = sample['prediction'].squeeze()
ncols = 2
fig, axs = plt.subplots(
nrows=1, ncols=ncols, figsize=(ncols * 4, 4), squeeze=False
)
axs[0, 0].imshow(mask, interpolation='none')
axs[0, 0].axis('off')
if show_titles:
axs[0, 0].set_title('Mask')
if showing_predictions:
axs[0, 1].imshow(pred, interpolation='none')
axs[0, 1].axis('off')
if show_titles:
axs[0, 1].set_title('Prediction')
if suptitle is not None:
plt.suptitle(suptitle)
return fig