So2Sat#

class torchgeo.datasets.So2Sat(root='data', version='2', split='train', bands=('S1_B1', 'S1_B2', 'S1_B3', 'S1_B4', 'S1_B5', 'S1_B6', 'S1_B7', 'S1_B8', 'S2_B02', 'S2_B03', 'S2_B04', 'S2_B05', 'S2_B06', 'S2_B07', 'S2_B08', 'S2_B8A', 'S2_B11', 'S2_B12'), transforms=None, checksum=False)[source]#

Bases: NonGeoDataset

So2Sat dataset.

The So2Sat dataset consists of corresponding synthetic aperture radar and multispectral optical image data acquired by the Sentinel-1 and Sentinel-2 remote sensing satellites, and a corresponding local climate zones (LCZ) label. The dataset is distributed over 42 cities across different continents and cultural regions of the world, and comes with a variety of different splits.

This implementation covers the 2nd and 3rd versions of the dataset as described in the author’s github repository: zhu-xlab/So2Sat-LCZ42.

The different versions are as follows:

Version 2: This version contains imagery from 52 cities and is split into train/val/test as follows:

Training: 42 cities around the world
Validation: western half of 10 other cities covering 10 cultural zones
Testing: eastern half of the 10 other cities

Version 3: A version of the dataset with 3 different train/test splits, as follows:

Random split: every city 80% training / 20% testing (randomly sampled)
Block split: every city is split in a geospatial 80%/20%-manner
Cultural 10: 10 cities from different cultural zones are held back for testing purposes

Dataset classes:

Compact high rise
Compact middle rise
Compact low rise
Open high rise
Open mid rise
Open low rise
Lightweight low rise
Large low rise
Sparsely built
Heavy industry
Dense trees
Scattered trees
Bush, scrub
Low plants
Bare rock or paved
Bare soil or sand
Water

If you use this dataset in your research, please cite the following paper:

https://doi.org/10.1109/MGRS.2020.2964708

Note

The version 2 dataset can be automatically downloaded using the following bash script:

for split in training validation testing
do
    wget ftp://m1483140:[email protected]/$split.h5
done

or manually downloaded from https://dataserv.ub.tum.de/index.php/s/m1483140 This download will likely take several hours.

The version 3 datasets can be downloaded using the following bash script:

for version in random block culture_10
do
  for split in training testing
  do
    wget -P $version/ ftp://m1613658:[email protected]/$version/$split.h5
  done
done

or manually downloaded from https://mediatum.ub.tum.de/1613658

Note

This dataset requires the following additional library to be installed:

https://pypi.org/project/h5py/ to load the dataset

__init__(root='data', version='2', split='train', bands=('S1_B1', 'S1_B2', 'S1_B3', 'S1_B4', 'S1_B5', 'S1_B6', 'S1_B7', 'S1_B8', 'S2_B02', 'S2_B03', 'S2_B04', 'S2_B05', 'S2_B06', 'S2_B07', 'S2_B08', 'S2_B8A', 'S2_B11', 'S2_B12'), transforms=None, checksum=False)[source]#

Initialize a new So2Sat dataset instance.

Parameters:

root (str | PathLike[str]) – root directory where dataset can be found
version (str) – one of “2”, “3_random”, “3_block”, or “3_culture_10”
split (str) – one of “train”, “validation”, or “test”
bands (Sequence[str]) – a sequence of band names to use where the indices correspond to the array index of combined Sentinel 1 and Sentinel 2
transforms (Callable[[dict[str, Any]], dict[str, Any]] | None) – a function/transform that takes input sample and its target as entry and returns a transformed version
checksum (bool) – if True, check the MD5 of the downloaded files (may be slow)

Raises:

AssertionError – if split argument is invalid
DatasetNotFoundError – If dataset is not found.
DependencyNotFoundError – If h5py is not installed.

Added in version 0.3: The bands parameter.

Added in version 0.5: The version parameter.

__getitem__(index)[source]#

Return an index within the dataset.

Parameters:: index (int) – index to return
Returns:: data and label at that index
Return type:: dict[str, Any]

__len__()[source]#

Return the number of data points in the dataset.

Returns:: length of the dataset
Return type:: int

plot(sample, show_titles=True, suptitle=None)[source]#

Plot a sample from the dataset.

Parameters:

sample (dict[str, Any]) – a sample returned by __getitem__()
show_titles (bool) – flag indicating whether to show titles above each panel
suptitle (str | None) – 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.

Return type:

Figure

Added in version 0.2.

__annotate_func__()#: The type of the None singleton.

So2Sat#

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