SimForest: RGBD Instance Segmentation Dataset

Autonomous perception in forest environments requires accurate detection and segmentation of complex natural objects such as trees, rocks, and terrain features. However, the scarcity of large-scale, annotated forest datasets, especially those with depth and instance segmentation labels, hinders progress in deploying robust deep learning models for forestry applications. In this paper, we present SimForest, a 4K-resolution synthetic RGBD dataset generated using a photorealistic forestry simulator built on Unreal Engine 5. SimForest comprises 5,000 images, each annotated with aligned RGB data, depth maps, instance segmentation masks, and detailed metadata including object poses, terrain depth, camera parameters, and environmental conditions such as season, time, and cloudiness. The virtual scenes are geo-located and seasonally matched to a real forest near Umeå, Sweden. To demonstrate the utility of SimForest, we conduct an experimental study involving the detection and segmentation of tree trunks using YOLOv11-based models trained on SimForest data. The evaluation shows strong detection accuracy (mAP@50 of 0.92) and solid segmentation performance (mAP@50 of 0.74). These findings highlight the potential of SimForest as a valuable resource for near-field RGBD perception in forestry and related outdoor robotics applications.