GlobalBuildingMap#
- class torchgeo.datasets.GlobalBuildingMap(paths='data', crs=None, res=None, bands=None, transforms=None, cache=True, time_series=False)[source]#
Bases:
RasterDatasetGlobalBuildingMap dataset.
The GlobalBuildingMap (GBM) dataset provides the highest resolution and highest accuracy building footprint map on a global scale ever created. GBM was generated by training and applying modern deep neural networks on nearly 800,000 satellite images. The dataset is stored in 5 by 5 degree tiles in geotiff format.
The GlobalBuildingMap is generated by applying an ensemble of deep neural networks on nearly 800,000 satellite images of about 3m resolution. The deep neural networks were trained with manually inspected training samples generated from OpenStreetMap.
If you use this dataset in your research, please cite the following paper:
Added in version 0.7.
- filename_glob = 'GBM_v1_*'#
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.
- is_image = False#
True if the dataset only contains model inputs (such as images). False if the dataset only contains ground truth model outputs (such as segmentation masks).
The sample returned by the dataset/data loader will use the “image” key if is_image is True, otherwise it will use the “mask” key.
For datasets with both model inputs and outputs, the recommended approach is to use 2 RasterDataset instances and combine them using an IntersectionDataset.