These combinations require that all queries are present in both datasets, and can be created using an IntersectionDataset. TorchGeo automatically composes these datasets for you when you use the intersection (&) and union (|) operators.
Multispectral and geospatial transforms
In deep learning, it's common to augment and transform the data so that models are robust to variations in the input space. Geospatial data can have variations such as seasonal changes and warping effects, as well as image processing and capture issues like cloud cover and atmospheric distortion. TorchGeo utilizes augmentations and transforms from the Kornia library, which supports GPU acceleration and supports multispectral imagery with more than 3 channels.