Scaling Up Forest Vision with Synthetic Data.

Area of Science:

• Forestry and Remote Sensing
• Artificial Intelligence
• Computer Vision

Background:

• Accurate individual tree segmentation is vital for ecosystem function analysis, particularly carbon cycling.
• Advancements in AI have improved tree segmentation, but limited public 3D forest datasets hinder robust system development.

Purpose of the Study:

• To investigate the utility of synthetic data for pretraining tree segmentation models, thereby reducing reliance on extensive real-world data annotation.
• To develop a novel synthetic data generation pipeline, CAMP3D, for 3D forest vision tasks.

Main Methods:

• Developed the Cambridge Arboreal Modelling Panoptic 3D (CAMP3D) pipeline, integrating game engines and physics-based LiDAR simulation.
• Generated a large-scale, diverse, annotated synthetic 3D forest dataset.
• Pretrained a state-of-the-art tree segmentation algorithm on synthetic data and fine-tuned it with minimal real forest plot data.

Main Results:

• Synthetic data pretraining substantially decreased the requirement for labeled real forest data.
• A model fine-tuned on a single, small real forest plot achieved segmentation performance competitive with models trained on extensive real data.
• Identified physics, diversity, and scale as critical factors for effective synthetic data utilization.

Conclusions:

• Synthetic data, generated via pipelines like CAMP3D, offers a scalable solution to overcome data limitations in 3D forest vision.
• This approach paves the way for more robust and efficient 3D forest analysis systems.
• The CAMP3D pipeline and dataset are publicly available to advance research.