FinnWoodlands Dataset
This dataset addresses the lack of diverse datasets for forestry applications, enabling data-driven methods for forest-like scenarios, though it is incremental as it focuses on a specific domain.
The paper introduces the FinnWoodlands dataset, a forest dataset with RGB stereo images, point clouds, and sparse depth maps, including manual annotations for semantic, instance, and panoptic segmentation, comprising 4226 objects with 2562 tree trunks classified into three categories, and provides an initial benchmark using three models to illustrate challenges in unstructured scenarios.
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.