# Copyright (c) TorchGeo Contributors. All rights reserved.
# Licensed under the MIT License.
"""Western USA Live Fuel Moisture Dataset."""
import glob
import json
import os
from collections.abc import Callable, Iterable
import matplotlib.pyplot as plt
import pandas as pd
import torch
from matplotlib.figure import Figure
from .errors import DatasetNotFoundError
from .geo import NonGeoDataset
from .utils import Path, Sample, which
[docs]
class WesternUSALiveFuelMoisture(NonGeoDataset):
"""Western USA Live Fuel Moisture Dataset.
This tabular style dataset contains fuel moisture
(mass of water in vegetation) and remotely sensed variables
in the western United States. It contains 2615 datapoints and 138
variables. For more details see the
`dataset page <https://source.coop/stanford/sar-moisture-conent>`_.
Dataset Format:
* .geojson file for each datapoint
Dataset Features:
* 138 remote sensing derived variables, some with a time dependency
* 2615 datapoints with regression target of predicting fuel moisture
If you use this dataset in your research, please cite the following paper:
* https://doi.org/10.1016/j.rse.2020.111797
.. 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/su-sar-moisture-content'
label_name = 'percent(t)'
all_variable_names = (
# "date",
'slope(t)',
'elevation(t)',
'canopy_height(t)',
'forest_cover(t)',
'silt(t)',
'sand(t)',
'clay(t)',
'vv(t)',
'vh(t)',
'red(t)',
'green(t)',
'blue(t)',
'swir(t)',
'nir(t)',
'ndvi(t)',
'ndwi(t)',
'nirv(t)',
'vv_red(t)',
'vv_green(t)',
'vv_blue(t)',
'vv_swir(t)',
'vv_nir(t)',
'vv_ndvi(t)',
'vv_ndwi(t)',
'vv_nirv(t)',
'vh_red(t)',
'vh_green(t)',
'vh_blue(t)',
'vh_swir(t)',
'vh_nir(t)',
'vh_ndvi(t)',
'vh_ndwi(t)',
'vh_nirv(t)',
'vh_vv(t)',
'slope(t-1)',
'elevation(t-1)',
'canopy_height(t-1)',
'forest_cover(t-1)',
'silt(t-1)',
'sand(t-1)',
'clay(t-1)',
'vv(t-1)',
'vh(t-1)',
'red(t-1)',
'green(t-1)',
'blue(t-1)',
'swir(t-1)',
'nir(t-1)',
'ndvi(t-1)',
'ndwi(t-1)',
'nirv(t-1)',
'vv_red(t-1)',
'vv_green(t-1)',
'vv_blue(t-1)',
'vv_swir(t-1)',
'vv_nir(t-1)',
'vv_ndvi(t-1)',
'vv_ndwi(t-1)',
'vv_nirv(t-1)',
'vh_red(t-1)',
'vh_green(t-1)',
'vh_blue(t-1)',
'vh_swir(t-1)',
'vh_nir(t-1)',
'vh_ndvi(t-1)',
'vh_ndwi(t-1)',
'vh_nirv(t-1)',
'vh_vv(t-1)',
'slope(t-2)',
'elevation(t-2)',
'canopy_height(t-2)',
'forest_cover(t-2)',
'silt(t-2)',
'sand(t-2)',
'clay(t-2)',
'vv(t-2)',
'vh(t-2)',
'red(t-2)',
'green(t-2)',
'blue(t-2)',
'swir(t-2)',
'nir(t-2)',
'ndvi(t-2)',
'ndwi(t-2)',
'nirv(t-2)',
'vv_red(t-2)',
'vv_green(t-2)',
'vv_blue(t-2)',
'vv_swir(t-2)',
'vv_nir(t-2)',
'vv_ndvi(t-2)',
'vv_ndwi(t-2)',
'vv_nirv(t-2)',
'vh_red(t-2)',
'vh_green(t-2)',
'vh_blue(t-2)',
'vh_swir(t-2)',
'vh_nir(t-2)',
'vh_ndvi(t-2)',
'vh_ndwi(t-2)',
'vh_nirv(t-2)',
'vh_vv(t-2)',
'slope(t-3)',
'elevation(t-3)',
'canopy_height(t-3)',
'forest_cover(t-3)',
'silt(t-3)',
'sand(t-3)',
'clay(t-3)',
'vv(t-3)',
'vh(t-3)',
'red(t-3)',
'green(t-3)',
'blue(t-3)',
'swir(t-3)',
'nir(t-3)',
'ndvi(t-3)',
'ndwi(t-3)',
'nirv(t-3)',
'vv_red(t-3)',
'vv_green(t-3)',
'vv_blue(t-3)',
'vv_swir(t-3)',
'vv_nir(t-3)',
'vv_ndvi(t-3)',
'vv_ndwi(t-3)',
'vv_nirv(t-3)',
'vh_red(t-3)',
'vh_green(t-3)',
'vh_blue(t-3)',
'vh_swir(t-3)',
'vh_nir(t-3)',
'vh_ndvi(t-3)',
'vh_ndwi(t-3)',
'vh_nirv(t-3)',
'vh_vv(t-3)',
'lat',
'lon',
)
[docs]
def __init__(
self,
root: Path = 'data',
input_features: Iterable[str] = all_variable_names,
transforms: Callable[[Sample], Sample] | None = None,
download: bool = False,
) -> None:
"""Initialize a new Western USA Live Fuel Moisture Dataset.
Args:
root: root directory where dataset can be found
input_features: which input features to include
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 ``input_features`` contains invalid variable names
DatasetNotFoundError: If dataset is not found and *download* is False.
"""
assert set(input_features) <= set(self.all_variable_names)
self.root = root
self.input_features = input_features
self.transforms = transforms
self.download = download
self._verify()
self.dataframe = self._load_data()
[docs]
def __len__(self) -> int:
"""Return the number of data points in the dataset.
Returns:
length of the dataset
"""
return len(self.dataframe)
[docs]
def __getitem__(self, index: int) -> Sample:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
input features and target at that index
"""
data = self.dataframe.iloc[index, :]
sample = {
'input': torch.tensor(
data.drop([self.label_name]).values, dtype=torch.float32
),
'label': torch.tensor(data[self.label_name], dtype=torch.float32),
}
if self.transforms is not None:
sample = self.transforms(sample)
return sample
def _load_data(self) -> pd.DataFrame:
"""Load data from individual files into pandas dataframe.
Returns:
the features and label
"""
data_rows = []
for path in sorted(self.files):
with open(path) as f:
content = json.load(f)
data_dict = content['properties']
data_dict['lon'] = content['geometry']['coordinates'][0]
data_dict['lat'] = content['geometry']['coordinates'][1]
data_rows.append(data_dict)
df = pd.DataFrame(data_rows)
df = df[[*self.input_features, self.label_name]]
return df
def _verify(self) -> None:
"""Verify the integrity of the dataset."""
# Check if the files already exist
file_glob = os.path.join(self.root, '**', 'feature_*.geojson')
self.files = glob.glob(file_glob, recursive=True)
if self.files:
return
# Check if the user requested to download the dataset
if not self.download:
raise DatasetNotFoundError(self)
# Download the dataset
self._download()
self.files = glob.glob(file_glob, recursive=True)
def _download(self) -> None:
"""Download the dataset and extract it."""
os.makedirs(self.root, exist_ok=True)
azcopy = which('azcopy')
azcopy('sync', self.url, self.root, '--recursive=true')
[docs]
def plot(
self,
sample: Sample,
variables_to_plot: list[str] | None = None,
show_titles: bool = True,
suptitle: str | None = None,
) -> Figure:
"""Plot a time series visualization of the LFMC sample.
Args:
sample: a sample returned by :meth:`__getitem__`
variables_to_plot: a list of valid variable to be drawn in the plot
show_titles: flag indicating whether to show titles above each panel
suptitle: optional suptitle to use for the Figure
Returns:
a matplotlib Figure with the rendered sample
.. versionadded:: 0.8
"""
if not variables_to_plot:
variables_to_plot = [
'slope',
'elevation',
'canopy_height',
'forest_cover',
'silt',
'sand',
'clay',
'vv',
'vh',
'red',
'green',
'blue',
'swir',
'nir',
'ndvi',
'ndwi',
'nirv',
'vv_red',
'vv_green',
'vv_blue',
'vv_swir',
'vv_nir',
'vv_ndvi',
'vv_ndwi',
'vv_nirv',
'vh_red',
'vh_green',
'vh_blue',
'vh_swir',
'vh_nir',
'vh_ndvi',
'vh_ndwi',
'vh_nirv',
'vh_vv',
]
input_data = sample['input'].numpy()
# Time points to display on x-axis
time_labels = ['t', 't-1', 't-2', 't-3']
fig, axs = plt.subplots(
len(variables_to_plot),
1,
figsize=(6, 1.5 * len(variables_to_plot)),
sharex=True,
)
# Handle single subplot case
if len(variables_to_plot) == 1:
axs = [axs]
for i, var_base_name in enumerate(variables_to_plot):
values = []
# Extract data for each time point (t, t-1, t-2, t-3)
for t_label in time_labels:
full_var_name = f'{var_base_name}({t_label})'
var_position = self.all_variable_names.index(full_var_name)
values.append(input_data[var_position])
axs[i].plot(range(len(time_labels)), values, 'o-')
axs[i].grid(True, alpha=0.3)
if show_titles:
axs[i].set_title(f'{var_base_name.upper()}')
axs[-1].set_xticks(range(len(time_labels)))
axs[-1].set_xticklabels(time_labels)
# add coordinate and label information below the plot
lon = input_data[-2]
lat = input_data[-1]
lfmc_value = sample['label'].item()
axs[-1].text(
x=0.5,
y=-0.7,
s=f'Live Fuel Moisture Content\nat {lon:.4f}, {lat:.4f}: {lfmc_value:.2f}%',
ha='center',
transform=axs[-1].transAxes,
)
if suptitle is not None:
fig.suptitle(t=suptitle, y=1.6, transform=axs[0].transAxes)
plt.tight_layout()
return fig
