  Unmanned aircraft have decreased the cost required to collect remote sensing
imagery, which has enabled researchers to collect high-spatial resolution data
from multiple sensor modalities more frequently and easily. The increase in
data will push the need for semantic segmentation frameworks that are able to
classify non-RGB imagery, but this type of algorithmic development requires an
increase in publicly available benchmark datasets with class labels. In this
paper, we introduce a high-resolution multispectral dataset with image labels.
This new benchmark dataset has been pre-split into training/testing folds in
order to standardize evaluation and continue to push state-of-the-art
classification frameworks for non-RGB imagery.
