Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest
This addresses the challenge of accurate forest monitoring for forestry management, but it is incremental as it builds on existing methods like PointNet++ and graph-based approaches.
The authors tackled the problem of parameter tuning for semantic and instance segmentation in mobile laser scanning data of coniferous forests, achieving a 0.92 F1-score in semantic segmentation and a 0.6 F1-score in instance segmentation, with optimization boosting performance by about 4%.
This article introduces Point2Tree, a novel framework that incorporates a three-stage process involving semantic segmentation, instance segmentation, optimization analysis of hyperparemeters importance. It introduces a comprehensive and modular approach to processing laser points clouds in Forestry. We tested it on two independent datasets. The first area was located in an actively managed boreal coniferous dominated forest in Våler, Norway, 16 circular plots of 400 square meters were selected to cover a range of forest conditions in terms of species composition and stand density. We trained a model based on Pointnet++ architecture which achieves 0.92 F1-score in semantic segmentation. As a second step in our pipeline we used graph-based approach for instance segmentation which reached F1-score approx. 0.6. The optimization allowed to further boost the performance of the pipeline by approx. 4 \% points.