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

2025-11-09

AI Summary

Forest-related 3D point cloud analysis—encompassing instance segmentation, semantic segmentation, and tree species classification—suffers from heavy reliance on large-scale annotated datasets. Method: This paper proposes an integrated framework unifying self-supervised pretraining, domain adaptation, and hierarchical transfer learning. It enables cross-scene knowledge transfer via unsupervised point cloud representation learning, enhances generalization to unseen tree species through hierarchical feature disentanglement, and jointly models multi-task outputs. Results: Experiments demonstrate substantial improvements: +16.98% in instance segmentation AP₅₀, +1.79% in semantic segmentation mIoU, and +6.07% in tree species classification Jaccard index; annotation requirements are reduced by ~60%, and inference energy consumption drops by 21%. To our knowledge, this is the first work to deeply co-model all three tasks within a lightweight, open-source framework, significantly advancing fine-grained perception and carbon sink monitoring accuracy in complex forest environments.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Large pretrained models with web data
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

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.

Problem

• Reducing dependency on annotated 3D forest point clouds
• Improving instance and semantic segmentation of individual trees
• Enabling accurate species classification with limited labeled data

Innovation

• Uses self-supervised learning to reduce annotation needs
• Applies transfer learning for accurate species classification
• Integrates tasks into a unified framework for efficiency