Source code for torchgeo.datasets.rwanda_field_boundary
# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""Rwanda Field Boundary Competition dataset."""
import glob
import os
from collections.abc import Callable, Sequence
from typing import ClassVar
import matplotlib.pyplot as plt
import numpy as np
import rasterio
import rasterio.features
import torch
from einops import rearrange
from matplotlib.figure import Figure
from torch import Tensor
from .errors import DatasetNotFoundError, RGBBandsMissingError
from .geo import NonGeoDataset
from .utils import Path, which
[docs]class RwandaFieldBoundary(NonGeoDataset):
"""Rwanda Field Boundary Competition dataset.
This dataset contains field boundaries for smallholder farms in eastern Rwanda.
The Nasa Harvest program funded a team of annotators from TaQadam to label Planet
imagery for the 2021 growing season for the purpose of conducting the Rwanda Field
boundary detection Challenge. The dataset includes rasterized labeled field
boundaries and time series satellite imagery from Planet's NICFI program.
Planet's basemap imagery is provided for six months (March, April, August, October,
November and December). Note: only fields that were big enough to be differentiated
on the Planetscope imagery were labeled, only fields that were fully contained
within the chips were labeled. The paired dataset is provided in 256x256 chips for a
total of 70 tiles covering 1532 individual fields.
The labels are provided as binary semantic segmentation labels:
0. No field-boundary
1. Field-boundary
If you use this dataset in your research, please cite the following:
* https://doi.org/10.34911/RDNT.G580WW
.. note::
This dataset requires the following additional library to be installed:
* `azcopy <https://github.com/Azure/azure-storage-azcopy>`_: to download the
dataset from Source Cooperative.
.. versionadded:: 0.5
"""
url = 'https://radiantearth.blob.core.windows.net/mlhub/nasa_rwanda_field_boundary_competition'
splits: ClassVar[dict[str, int]] = {'train': 57, 'test': 13}
dates = ('2021_03', '2021_04', '2021_08', '2021_10', '2021_11', '2021_12')
all_bands = ('B01', 'B02', 'B03', 'B04')
rgb_bands = ('B03', 'B02', 'B01')
classes = ('No field-boundary', 'Field-boundary')
[docs] def __init__(
self,
root: Path = 'data',
split: str = 'train',
bands: Sequence[str] = all_bands,
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
) -> None:
"""Initialize a new RwandaFieldBoundary instance.
Args:
root: root directory where dataset can be found
split: one of "train" or "test"
bands: the subset of bands to load
transforms: a function/transform that takes input sample and its target as
entry and returns a transformed version
download: if True, download dataset and store it in the root directory
Raises:
AssertionError: If *split* or *bands* are invalid.
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
assert split in self.splits
assert set(bands) <= set(self.all_bands)
self.root = root
self.split = split
self.bands = bands
self.transforms = transforms
self.download = download
self._verify()
[docs] def __len__(self) -> int:
"""Return the number of chips in the dataset.
Returns:
length of the dataset
"""
return self.splits[self.split]
[docs] def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
a dict containing image and mask at index.
"""
images = []
for date in self.dates:
patches = []
for band in self.bands:
path = os.path.join(self.root, 'source', self.split, date)
with rasterio.open(os.path.join(path, f'{index:02}_{band}.tif')) as src:
patches.append(src.read(1).astype(np.float32))
images.append(patches)
sample = {'image': torch.from_numpy(np.array(images))}
if self.split == 'train':
path = os.path.join(self.root, 'labels', self.split)
with rasterio.open(os.path.join(path, f'{index:02}.tif')) as src:
sample['mask'] = torch.from_numpy(src.read(1).astype(np.int64))
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the subdirectories already exist and have the correct number of files
path = os.path.join(self.root, 'source', self.split, '*', '*.tif')
expected = len(self.dates) * self.splits[self.split] * len(self.all_bands)
if len(glob.glob(path)) == expected:
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download and extract the dataset
self._download()
def _download(self) -> None:
"""Download the dataset."""
os.makedirs(self.root, exist_ok=True)
azcopy = which('azcopy')
azcopy('sync', self.url, self.root, '--recursive=true')
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
time_step: int = 0,
suptitle: str | None = None,
) -> Figure:
"""Plot a sample from the dataset.
Args:
sample: a sample returned by :meth:`__getitem__`
show_titles: flag indicating whether to show titles above each panel
time_step: time step at which to access image, beginning with 0
suptitle: optional string to use as a suptitle
Returns:
a matplotlib Figure with the rendered sample
Raises:
RGBBandsMissingError: If *bands* does not include all RGB bands.
"""
rgb_indices = []
for band in self.rgb_bands:
if band in self.bands:
rgb_indices.append(self.bands.index(band))
else:
raise RGBBandsMissingError()
ncols = 1
for key in ('mask', 'prediction'):
if key in sample:
ncols += 1
fig, axs = plt.subplots(ncols=ncols, squeeze=False)
image = torch.clamp(sample['image'][time_step, rgb_indices] / 2000, 0, 1)
image = rearrange(image, 'c h w -> h w c')
axs[0, 0].imshow(image)
axs[0, 0].axis('off')
if show_titles:
axs[0, 0].set_title(f't={time_step}')
if 'mask' in sample:
axs[0, 1].imshow(sample['mask'])
axs[0, 1].axis('off')
if show_titles:
axs[0, 1].set_title('Mask')
if 'prediction' in sample:
axs[0, 2].imshow(sample['prediction'])
axs[0, 2].axis('off')
if show_titles:
axs[0, 2].set_title('Prediction')
if suptitle is not None:
fig.suptitle(suptitle)
return fig
