  While the availability of large and diverse datasets has contributed to
significant breakthroughs in autonomous driving and indoor applications,
forestry applications are still lagging behind and new forest datasets would
most certainly contribute to achieving significant progress in the development
of data-driven methods for forest-like scenarios. This paper introduces a
forest dataset called \textit{FinnWoodlands}, which consists of RGB stereo
images, point clouds, and sparse depth maps, as well as ground truth manual
annotations for semantic, instance, and panoptic segmentation.
\textit{FinnWoodlands} comprises a total of 4226 objects manually annotated,
out of which 2562 objects (60.6\%) correspond to tree trunks classified into
three different instance categories, namely "Spruce Tree", "Birch Tree", and
"Pine Tree". Besides tree trunks, we also annotated "Obstacles" objects as
instances as well as the semantic stuff classes "Lake", "Ground", and "Track".
Our dataset can be used in forestry applications where a holistic
representation of the environment is relevant. We provide an initial benchmark
using three models for instance segmentation, panoptic segmentation, and depth
completion, and illustrate the challenges that such unstructured scenarios
introduce.
