Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Individual, Structural, and Species Analysis

TL;DR

This work tackles the challenge of extracting detailed, tree-level structural and species information from 3D forest point clouds under limited annotation. It introduces a unified framework that combines self-supervised contrastive pretraining, domain adaptation, and hierarchical transfer learning to support instance segmentation, semantic segmentation, and species classification. Empirical results show strong performance under few-shot conditions, with notable gains in segmentation accuracy and species-level recognition, along with measurable reductions in training energy and carbon footprint. The open-source pipeline and diverse benchmarking datasets enable practical deployment for precision forestry, biodiversity monitoring, and carbon mapping.

Abstract

Detailed structural and species information on individual tree level is increasingly important to support precision forestry, biodiversity conservation, and provide reference data for biomass and carbon mapping. Point clouds from airborne and ground-based laser scanning are currently the most suitable data source to rapidly derive such information at scale. Recent advancements in deep learning improved segmenting and classifying individual trees and identifying semantic tree components. However, deep learning models typically require large amounts of annotated training data which limits further improvement. Producing dense, high-quality annotations for 3D point clouds, especially in complex forests, is labor-intensive and challenging to scale. We explore strategies to reduce dependence on large annotated datasets using self-supervised and transfer learning architectures. Our objective is to improve performance across three tasks: instance segmentation, semantic segmentation, and tree classification using realistic and operational training sets. Our findings indicate that combining self-supervised learning with domain adaptation significantly enhances instance segmentation compared to training from scratch (AP50 +16.98%), self-supervised learning suffices for semantic segmentation (mIoU +1.79%), and hierarchical transfer learning enables accurate classification of unseen species (Jaccard +6.07%). To simplify use and encourage uptake, we integrated the tasks into a unified framework, streamlining the process from raw point clouds to tree delineation, structural analysis, and species classification. Pretrained models reduce energy consumption and carbon emissions by ~21%. This open-source contribution aims to accelerate operational extraction of individual tree information from laser scanning point clouds to support forestry, biodiversity, and carbon mapping.

Figures (15)

• Figure 1: Overview of the framework.
• Figure 2: Examples of sampled points and trees under different labeling scenarios. [a,b,c] illustrate uniform label reduction, while [d,e,f] show tree-level label reduction.
• Figure 3: Instance segmentation pipeline
• Figure 4: Semantic segmentation pipeline
• Figure 5: Tree classification pipeline