One of the goals of TorchGeo is to provide easy-to-use data loaders for these existing datasets. TorchGeo includes a number of benchmark datasets —datasets that include both input images and target labels. This includes datasets for tasks like image classification, regression, semantic segmentation, object detection, instance segmentation, change detection, and more.
If you've used torchvision before, these types of datasets should be familiar. In this example, we'll create a dataset for the Northwestern Polytechnical University (NWPU) very-high-resolution ten-class (VHR-10) geospatial object detection dataset. This dataset can be automatically downloaded, checksummed, and extracted, just like with torchvision.
```c++
from torch.utils.data import DataLoader
from torchgeo.datasets import VHR10
dataset = VHR10(root="...", download=True, checksum=True)