Open Buildings#

class torchgeo.datasets.OpenBuildings(paths='data', crs=None, res=0.0001, transforms=None, checksum=False)[source]#

Bases: VectorDataset

Open Buildings dataset.

The Open Buildings dataset consists of computer generated building detections across the African continent.

Dataset features:

  • 516M building detections as polygons with centroid lat/long

  • covering area of 19.4M km2 (64% of the African continent)

  • confidence score and Plus Code

Dataset format:

  • csv files containing building detections compressed as csv.gz

  • meta data geojson file

The data can be downloaded from here. Additionally, the meta data geometry file also needs to be placed in root as tiles.geojson.

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

Added in version 0.3.

filename_glob = '*_buildings.csv'#

Glob expression used to search for files.

This expression should be specific enough that it will not pick up files from other datasets. It should not include a file extension, as the dataset may be in a different file format than what it was originally downloaded as.

__init__(paths='data', crs=None, res=0.0001, transforms=None, checksum=False)[source]#

Initialize a new Dataset instance.

Parameters:

  • paths (str | PathLike[str] | Iterable[str | PathLike[str]]) – one or more root directories to search or files to load

  • crs (CRS | None) – coordinate reference system (CRS) to warp to (defaults to the CRS of the first file found)

  • res (float | tuple[float, float]) – resolution of the dataset in units of CRS in (xres, yres) format. If a single float is provided, it is used for both the x and y resolution.

  • 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:

DatasetNotFoundError – If dataset is not found.

Changed in version 0.5: root was renamed to paths.

__getitem__(index)[source]#

Retrieve input, target, and/or metadata indexed by spatiotemporal slice.

Parameters:

index (slice | tuple[slice] | tuple[slice, slice] | tuple[slice, slice, slice]) – [xmin:xmax:xres, ymin:ymax:yres, tmin:tmax:tres] coordinates to index.

Returns:

Sample of input, target, and/or metadata at that index.

Raises:

IndexError – If index is not found in the dataset.

Return type:

dict[str, Any]

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

Return type:

Figure

__annotate_func__()#

The type of the None singleton.