Source code for torchgeo.datasets.cms_mangrove_canopy
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""CMS Global Mangrove Canopy dataset."""
import os
from typing import Any, Callable, Optional, Union
import matplotlib.pyplot as plt
from matplotlib.figure import Figure
from rasterio.crs import CRS
from .geo import RasterDataset
from .utils import check_integrity, extract_archive
[docs]class CMSGlobalMangroveCanopy(RasterDataset):
"""CMS Global Mangrove Canopy dataset.
The `CMS Global Mangrove Canopy dataset
<https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1665>`_
consists of a single band map at 30m resolution of either aboveground biomass (agb),
basal area weighted height (hba95), or maximum canopy height (hmax95).
The dataset needs to be manually dowloaded from the above link, where you can make
an account and subsequently download the dataset.
.. versionadded:: 0.3
"""
is_image = False
filename_regex = r"""^
(?P<mangrove>[A-Za-z]{8})
_(?P<variable>[a-z0-9]*)
_(?P<country>[A-Za-z][^.]*)
"""
zipfile = "CMS_Global_Map_Mangrove_Canopy_1665.zip"
md5 = "3e7f9f23bf971c25e828b36e6c5496e3"
all_countries = [
"AndamanAndNicobar",
"Angola",
"Anguilla",
"AntiguaAndBarbuda",
"Aruba",
"Australia",
"Bahamas",
"Bahrain",
"Bangladesh",
"Barbados",
"Belize",
"Benin",
"Brazil",
"BritishVirginIslands",
"Brunei",
"Cambodia",
"Cameroon",
"CarribeanCaymanIslands",
"China",
"Colombia",
"Comoros",
"CostaRica",
"Cote",
"CoteDivoire",
"CotedIvoire",
"Cuba",
"DemocraticRepublicOfCongo",
"Djibouti",
"DominicanRepublic",
"EcuadorWithGalapagos",
"Egypt",
"ElSalvador",
"EquatorialGuinea",
"Eritrea",
"EuropaIsland",
"Fiji",
"Fiji2",
"FrenchGuiana",
"FrenchGuyana",
"FrenchPolynesia",
"Gabon",
"Gambia",
"Ghana",
"Grenada",
"Guadeloupe",
"Guam",
"Guatemala",
"Guinea",
"GuineaBissau",
"Guyana",
"Haiti",
"Hawaii",
"Honduras",
"HongKong",
"India",
"Indonesia",
"Iran",
"Jamaica",
"Japan",
"Kenya",
"Liberia",
"Macau",
"Madagascar",
"Malaysia",
"Martinique",
"Mauritania",
"Mayotte",
"Mexico",
"Micronesia",
"Mozambique",
"Myanmar",
"NewCaledonia",
"NewZealand",
"Newzealand",
"Nicaragua",
"Nigeria",
"NorthernMarianaIslands",
"Oman",
"Pakistan",
"Palau",
"Panama",
"PapuaNewGuinea",
"Peru",
"Philipines",
"PuertoRico",
"Qatar",
"ReunionAndMauritius",
"SaintKittsAndNevis",
"SaintLucia",
"SaintVincentAndTheGrenadines",
"Samoa",
"SaudiArabia",
"Senegal",
"Seychelles",
"SierraLeone",
"Singapore",
"SolomonIslands",
"Somalia",
"Somalia2",
"Soudan",
"SouthAfrica",
"SriLanka",
"Sudan",
"Suriname",
"Taiwan",
"Tanzania",
"Thailand",
"TimorLeste",
"Togo",
"Tonga",
"TrinidadAndTobago",
"TurksAndCaicosIslands",
"Tuvalu",
"UnitedArabEmirates",
"UnitedStates",
"Vanuatu",
"Venezuela",
"Vietnam",
"VirginIslandsUs",
"WallisAndFutuna",
"Yemen",
]
measurements = ["agb", "hba95", "hmax95"]
[docs] def __init__(
self,
paths: Union[str, list[str]] = "data",
crs: Optional[CRS] = None,
res: Optional[float] = None,
measurement: str = "agb",
country: str = all_countries[0],
transforms: Optional[Callable[[dict[str, Any]], dict[str, Any]]] = None,
cache: bool = True,
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)
measurement: which of the three measurements, 'agb', 'hba95', or 'hmax95'
country: country for which to retrieve data
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
checksum: if True, check the MD5 of the downloaded files (may be slow)
Raises:
FileNotFoundError: if no files are found in ``paths``
RuntimeError: if dataset is missing or checksum fails
AssertionError: if country or measurement arg are not str or invalid
.. versionchanged:: 0.5
*root* was renamed to *paths*.
"""
self.paths = paths
self.checksum = checksum
assert isinstance(country, str), "Country argument must be a str."
assert (
country in self.all_countries
), "You have selected an invalid country, please choose one of {}".format(
self.all_countries
)
self.country = country
assert isinstance(measurement, str), "Measurement must be a string."
assert (
measurement in self.measurements
), "You have entered an invalid measurement, please choose one of {}.".format(
self.measurements
)
self.measurement = measurement
self.filename_glob = f"**/Mangrove_{self.measurement}_{self.country}*"
self._verify()
super().__init__(paths, crs, res, transforms=transforms, cache=cache)
def _verify(self) -> None:
"""Verify the integrity of the dataset.
Raises:
RuntimeError: if dataset is missing or checksum fails
"""
# Check if the extracted files already exist
if self.files:
return
# Check if the zip file has already been downloaded
assert isinstance(self.paths, str)
pathname = os.path.join(self.paths, self.zipfile)
if os.path.exists(pathname):
if self.checksum and not check_integrity(pathname, self.md5):
raise RuntimeError("Dataset found, but corrupted.")
self._extract()
return
raise RuntimeError(
f"Dataset not found in `paths={self.paths!r}` "
"either specify a different `paths` or make sure you "
"have manually downloaded the dataset as instructed in the documentation."
)
def _extract(self) -> None:
"""Extract the dataset."""
assert isinstance(self.paths, str)
pathname = os.path.join(self.paths, self.zipfile)
extract_archive(pathname)
[docs] def plot(
self,
sample: dict[str, Any],
show_titles: bool = True,
suptitle: Optional[str] = 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))
if showing_predictions:
axs[0].imshow(mask)
axs[0].axis("off")
axs[1].imshow(pred)
axs[1].axis("off")
if show_titles:
axs[0].set_title("Mask")
axs[1].set_title("Prediction")
else:
axs.imshow(mask)
axs.axis("off")
if show_titles:
axs.set_title("Mask")
if suptitle is not None:
plt.suptitle(suptitle)
return fig
