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FinnWoodlands Dataset

This paper introduces a forest dataset called 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.

Read the full paper here:
https://link.springer.com/chapter/10.1007/978-3-031-31435-3_7

The data

To download the subset with annotations follow this link:
https://drive.google.com/file/d/1uf9QBv1j_VRjM6jWCp2cgw5ZQ5CUx9R7/view?usp=share_link

To download all data (no GT annotations), including RGB stereo frames, point clouds and sparse depth maps follow this link:
https://drive.google.com/drive/folders/1RhLxuHoxfB5C-Nz2_oyVAX1ima02Ddt8?usp=share_link

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Screenshot from 2023-05-16 16-30-34

FinnWoodlands

Screenshot from 2023-05-16 15-50-01

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Cite

Please cite this work using:

Lagos, J., Lempiö, U., & Rahtu, E. (2023). FinnWoodlands Dataset. In R. Gade, M. Felsberg, & J.-K. Kämäräinen (Eds.), Image Analysis (pp. 95-110). Cham: Springer Nature Switzerland.

Abstract: 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 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. 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. FinnWoodlands dataset is available at GitHub.

Cite using .bib
https://citation-needed.springer.com/v2/references/10.1007/978-3-031-31435-3_7?format=bibtex&flavour=citation

Thank you!