This dataset includes a drone (Uncrewed Aerial Vehicles, UAV) orthomosaic (RGB) of plant communities acquired at the Mueller Glacier (New Zealand) in Februrary 2018. The resolution (ground sampling distance) of the orthomosaic amounts to approx. 3-4 cm. The orthomosaic is partially labelled (polygon shapefiles) in terms of plant community cover. The plant communities have been defined according to a field survey (see reference below). The orthomosaic comes with an AOI (area of interest, polygon shapefile) that indicates the areas where the labelling was performed. Within the extent of this AOI plant communities are assumed to be completely delineated (by visual interpretation). For visual inspection of the imagery we recommend to generate image pyramids since the image data has a very high spatial resolution. Details on the dataset are mentioned in the corresponding publications: Kattenborn, T., Eichel, J., & Fassnacht, F. E. (2019). Convolutional Neural Networks enable efficient, accurate and fine-grained segmentation of plant species and communities from high-resolution UAV imagery. Scientific reports, 9(1), 1-9. https://doi.org/10.1038/s41598-019-53797-9 https://www.nature.com/articles/s41598-019-53797-9Kattenborn, T., Eichel, J., Wiser, S., Burrows, L., Fassnacht, F. E., & Schmidtlein, S. (2020). Convolutional Neural Networks accurately predict cover fractions of plant species and communities in Unmanned Aerial Vehicle imagery. Remote Sensing in Ecology and Conservation, 6(4), 472-486. https://doi.org/10.1002/rse2.146 https://zslpublications.onlinelibrary.wiley.com/doi/full/10.1002/rse2.146