Source code for torchgeo.datasets.rwanda_field_boundary
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""Rwanda Field Boundary Competition dataset."""
import os
from collections.abc import Sequence
from typing import Callable, Optional
import matplotlib.pyplot as plt
import numpy as np
import rasterio
import rasterio.features
import torch
from matplotlib.figure import Figure
from torch import Tensor
from .geo import NonGeoDataset
from .utils import check_integrity, download_radiant_mlhub_collection, extract_archive
[docs]class RwandaFieldBoundary(NonGeoDataset):
r"""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:
* `radiant-mlhub <https://pypi.org/project/radiant-mlhub/>`_ to download the
imagery and labels from the Radiant Earth MLHub
.. versionadded:: 0.5
"""
dataset_id = "nasa_rwanda_field_boundary_competition"
collection_ids = [
"nasa_rwanda_field_boundary_competition_source_train",
"nasa_rwanda_field_boundary_competition_labels_train",
"nasa_rwanda_field_boundary_competition_source_test",
]
number_of_patches_per_split = {"train": 57, "test": 13}
filenames = {
"train_images": "nasa_rwanda_field_boundary_competition_source_train.tar.gz",
"test_images": "nasa_rwanda_field_boundary_competition_source_test.tar.gz",
"train_labels": "nasa_rwanda_field_boundary_competition_labels_train.tar.gz",
}
md5s = {
"train_images": "1f9ec08038218e67e11f82a86849b333",
"test_images": "17bb0e56eedde2e7a43c57aa908dc125",
"train_labels": "10e4eb761523c57b6d3bdf9394004f5f",
}
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"]
splits = ["train", "test"]
[docs] def __init__(
self,
root: str = "data",
split: str = "train",
bands: Sequence[str] = all_bands,
transforms: Optional[Callable[[dict[str, Tensor]], dict[str, Tensor]]] = None,
download: bool = False,
api_key: Optional[str] = None,
checksum: 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
api_key: a RadiantEarth MLHub API key to use for downloading the dataset
checksum: if True, check the MD5 of the downloaded files (may be slow)
Raises:
RuntimeError: if ``download=False`` but dataset is missing or checksum fails
or if ``download=True`` and ``api_key=None``
"""
self._validate_bands(bands)
assert split in self.splits
if download and api_key is None:
raise RuntimeError("Must provide an API key to download the dataset")
self.root = os.path.expanduser(root)
self.bands = bands
self.transforms = transforms
self.split = split
self.download = download
self.api_key = api_key
self.checksum = checksum
self._verify()
self.image_filenames: list[list[list[str]]] = []
self.mask_filenames: list[str] = []
for i in range(self.number_of_patches_per_split[split]):
dates = []
for date in self.dates:
patch = []
for band in self.bands:
fn = os.path.join(
self.root,
f"nasa_rwanda_field_boundary_competition_source_{split}",
f"nasa_rwanda_field_boundary_competition_source_{split}_{i:02d}_{date}", # noqa: E501
f"{band}.tif",
)
patch.append(fn)
dates.append(patch)
self.image_filenames.append(dates)
self.mask_filenames.append(
os.path.join(
self.root,
f"nasa_rwanda_field_boundary_competition_labels_{split}",
f"nasa_rwanda_field_boundary_competition_labels_{split}_{i:02d}",
"raster_labels.tif",
)
)
[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, mask, transform, crs, and metadata at index.
"""
img_fns = self.image_filenames[index]
mask_fn = self.mask_filenames[index]
imgs = []
for date_fns in img_fns:
bands = []
for band_fn in date_fns:
with rasterio.open(band_fn) as f:
bands.append(f.read(1).astype(np.int32))
imgs.append(bands)
img = torch.from_numpy(np.array(imgs))
sample = {"image": img}
if self.split == "train":
with rasterio.open(mask_fn) as f:
mask = f.read(1)
mask = torch.from_numpy(mask)
sample["mask"] = mask
if self.transforms is not None:
sample = self.transforms(sample)
return sample
[docs] def __len__(self) -> int:
"""Return the number of chips in the dataset.
Returns:
length of the dataset
"""
return len(self.image_filenames)
def _validate_bands(self, bands: Sequence[str]) -> None:
"""Validate list of bands.
Args:
bands: user-provided sequence of bands to load
Raises:
ValueError: if an invalid band name is provided
"""
for band in bands:
if band not in self.all_bands:
raise ValueError(f"'{band}' is an invalid band name.")
def _verify(self) -> None:
"""Verify the integrity of the dataset.
Raises:
RuntimeError: if ``download=False`` but dataset is missing or checksum fails
"""
# Check if the subdirectories already exist and have the correct number of files
checks = []
for split, num_patches in self.number_of_patches_per_split.items():
path = os.path.join(
self.root, f"nasa_rwanda_field_boundary_competition_source_{split}"
)
if os.path.exists(path):
num_files = len(os.listdir(path))
# 6 dates + 1 collection.json file
checks.append(num_files == (num_patches * 6) + 1)
else:
checks.append(False)
if all(checks):
return
# Check if tar file already exists (if so then extract)
have_all_files = True
for group in ["train_images", "train_labels", "test_images"]:
filepath = os.path.join(self.root, self.filenames[group])
if os.path.exists(filepath):
if self.checksum and not check_integrity(filepath, self.md5s[group]):
raise RuntimeError("Dataset found, but corrupted.")
extract_archive(filepath)
else:
have_all_files = False
if have_all_files:
return
# Check if the user requested to download the dataset
if not self.download:
raise RuntimeError(
f"Dataset not found in `root={self.root}` and `download=False`, "
"either specify a different `root` directory or use `download=True` "
"to automatically download the dataset."
)
# Download and extract the dataset
self._download()
def _download(self) -> None:
"""Download the dataset and extract it.
Raises:
RuntimeError: if download doesn't work correctly or checksums don't match
"""
for collection_id in self.collection_ids:
download_radiant_mlhub_collection(collection_id, self.root, self.api_key)
for group in ["train_images", "train_labels", "test_images"]:
filepath = os.path.join(self.root, self.filenames[group])
if self.checksum and not check_integrity(filepath, self.md5s[group]):
raise RuntimeError("Dataset not found or corrupted.")
extract_archive(filepath, self.root)
[docs] def plot(
self,
sample: dict[str, Tensor],
show_titles: bool = True,
time_step: int = 0,
suptitle: Optional[str] = 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:
ValueError: if the RGB bands are not included in ``self.bands``
"""
rgb_indices = []
for band in self.rgb_bands:
if band in self.bands:
rgb_indices.append(self.bands.index(band))
else:
raise ValueError("Dataset doesn't contain some of the RGB bands")
num_time_points = sample["image"].shape[0]
assert time_step < num_time_points
image = np.rollaxis(sample["image"][time_step, rgb_indices].numpy(), 0, 3)
image = np.clip(image / 2000, 0, 1)
if "mask" in sample:
mask = sample["mask"].numpy()
else:
mask = np.zeros_like(image)
num_panels = 2
showing_predictions = "prediction" in sample
if showing_predictions:
predictions = sample["prediction"].numpy()
num_panels += 1
fig, axs = plt.subplots(ncols=num_panels, figsize=(4 * num_panels, 4))
axs[0].imshow(image)
axs[0].axis("off")
if show_titles:
axs[0].set_title(f"t={time_step}")
axs[1].imshow(mask, vmin=0, vmax=1, interpolation="none")
axs[1].axis("off")
if show_titles:
axs[1].set_title("Mask")
if showing_predictions:
axs[2].imshow(predictions, vmin=0, vmax=1, interpolation="none")
axs[2].axis("off")
if show_titles:
axs[2].set_title("Predictions")
if suptitle is not None:
plt.suptitle(suptitle)
return fig
