# FinnWoodlands Dataset

arXiv: https://arxiv.org/abs/2304.00793 · HTML: https://mlsanity.com/paper/2304.00793/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 36 references that are in the map.
- First posted on arXiv 3 Apr 2023.

## Builds on (most cited first, 20 of 20)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [TartanAir: A Dataset to Push the Limits of Visual SLAM](https://mlsanity.com/paper/2003.14338/) (2020)
- [Virtual KITTI 2](https://mlsanity.com/paper/2001.10773/) (2020)
- [Unity: A General Platform for Intelligent Agents](https://mlsanity.com/paper/1809.02627/) (2018)
- [A2D2: Audi Autonomous Driving Dataset](https://mlsanity.com/paper/2004.06320/) (2020)
- [RELLIS-3D Dataset: Data, Benchmarks and Analysis](https://mlsanity.com/paper/2011.12954/) (2020)
- [EfficientPS: Efficient Panoptic Segmentation](https://mlsanity.com/paper/2004.02307/) (2020)
- [In-Place Activated BatchNorm for Memory-Optimized Training of DNNs](https://mlsanity.com/paper/1712.02616/) (2017)
- [Learning Joint 2D-3D Representations for Depth Completion](https://mlsanity.com/paper/2012.12402/) (2020)
- [D2-City: A Large-Scale Dashcam Video Dataset of Diverse Traffic Scenarios](https://mlsanity.com/paper/1904.01975/) (2019)
- [Instance Segmentation for Autonomous Log Grasping in Forestry Operations](https://mlsanity.com/paper/2203.01902/) (2022)
- [Tree Detection and Diameter Estimation Based on Deep Learning](https://mlsanity.com/paper/2210.17424/) (2022)
- [SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences](https://mlsanity.com/paper/1904.01416/) (2019)
- [Everything You wanted to Know about Smart Agriculture](https://mlsanity.com/paper/2201.04754/) (2022)

## Built on by (most cited first, 8 of 8)

- [TimberVision: A Multi-Task Dataset and Framework for Log-Component Segmentation and Tracking in Autonomous Forestry Operations](https://mlsanity.com/paper/2501.07360/) (2025)
- [Survey on Datasets for Perception in Unstructured Outdoor Environments](https://mlsanity.com/paper/2404.18750/) (2024)
- [TOMD: A Trail-based Off-road Multimodal Dataset for Traversable Pathway Segmentation under Challenging Illumination Conditions](https://mlsanity.com/paper/2506.21630/) (2025)
- [The Impact of Semi-Supervised Learning on Line Segment Detection](https://mlsanity.com/paper/2411.04596/) (2024)
- [Diffusion-Based Image Augmentation for Semantic Segmentation in Outdoor Robotics](https://mlsanity.com/paper/2507.00153/) (2025)
- [WildfireX-SLAM: A Large-scale Low-altitude RGB-D Dataset for Wildfire SLAM and Beyond](https://mlsanity.com/paper/2510.27133/) (2025)
- [Granularity-Aware Transfer for Tree Instance Segmentation in Synthetic and Real Forests](https://mlsanity.com/paper/2604.13722/) (2026)
- [ProGuT: Label-Efficient Panoptic Segmentation for Forest Scenes](https://mlsanity.com/paper/2609.36891/) (2026)

## Abstract (arXiv)

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 textitFinnWoodlands, 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. textitFinnWoodlands 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.