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

• URL: http://arxiv.org/abs/2511.06331v1
• Date: Sun, 09 Nov 2025 11:16:20 GMT
• Title: Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Individual, Structural, and Species Analysis
• Authors: Aldino Rizaldy, Fabian Ewald Fassnacht, Ahmed Jamal Afifi, Hua Jiang, Richard Gloaguen, Pedram Ghamisi,
• Abstract summary: Point clouds from airborne and ground-based laser scanning are the most suitable data source to rapidly derive such information at scale.<n>Deep learning models typically require large amounts of annotated training data which limits further improvement.<n>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.
• Score: 13.179658020831683
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Learning Order Forest for Qualitative-Attribute Data Clustering [52.612779710298526]
  This paper discovers a tree-like distance structure to flexibly represent the local order relationship among intra-attribute qualitative values.<n>A joint learning mechanism is proposed to iteratively obtain more appropriate tree structures and clusters.<n>Experiments demonstrate that the joint learning adapts the forest to the clustering task to yield accurate results.
  arXiv  Detail & Related papers  (2026-03-03T07:49:50Z)
• TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree [52.44403214958304]
  In this paper, we introduce TreeLoRA, a novel approach that constructs layer-wise adapters by leveraging hierarchical gradient similarity.<n>To reduce the computational burden of task similarity estimation, we employ bandit techniques to develop an algorithm based on lower confidence bounds.<n> experiments on both vision transformers (ViTs) and large language models (LLMs) demonstrate the effectiveness and efficiency of our approach.
  arXiv  Detail & Related papers  (2025-06-12T05:25:35Z)
• Bridging Classical and Modern Computer Vision: PerceptiveNet for Tree Crown Semantic Segmentation [0.0]
  PerceptiveNet is a novel model incorporating a Logarithmic Gabor- parameterised convolutional layer with trainable filter parameters.<n>We investigate the impact of Log-Gabor, Gabor, and standard convolutional layers on semantic segmentation performance.<n>Our results outperform state-of-the-art models, demonstrating significant performance improvements on a tree crown dataset.
  arXiv  Detail & Related papers  (2025-05-29T16:11:08Z)
• A Closer Look at Deep Learning Methods on Tabular Datasets [78.61845513154502]
  We present an extensive study on TALENT, a collection of 300+ datasets spanning broad ranges of size.<n>Our evaluation shows that ensembling benefits both tree-based and neural approaches.
  arXiv  Detail & Related papers  (2024-07-01T04:24:07Z)
• Towards general deep-learning-based tree instance segmentation models [0.0]
  Deep-learning methods have been proposed which show the potential of learning to segment trees. We use seven diverse datasets found in literature to gain insights into the generalization capabilities under domain-shift. Our results suggest that a generalization from coniferous dominated sparse point clouds to deciduous dominated high-resolution point clouds is possible.
  arXiv  Detail & Related papers  (2024-05-03T12:42:43Z)
• PureForest: A Large-Scale Aerial Lidar and Aerial Imagery Dataset for Tree Species Classification in Monospecific Forests [0.0]
  We present the PureForest dataset: a large-scale, open, multimodal dataset designed for tree species classification. Most current public Lidar datasets for tree species classification have low diversity as they only span a small area of a few dozen annotated hectares at most. In contrast, PureForest has 18 tree species grouped into 13 semantic classes, and spans 339 km$2$ across 449 distinct monospecific forests.
  arXiv  Detail & Related papers  (2024-04-18T10:23:10Z)
• Bidirectional Knowledge Reconfiguration for Lightweight Point Cloud Analysis [74.00441177577295]
  Point cloud analysis faces computational system overhead, limiting its application on mobile or edge devices. This paper explores feature distillation for lightweight point cloud models. We propose bidirectional knowledge reconfiguration to distill informative contextual knowledge from the teacher to the student.
  arXiv  Detail & Related papers  (2023-10-08T11:32:50Z)
• TreeLearn: A deep learning method for segmenting individual trees from ground-based LiDAR forest point clouds [40.46280139210502]
  TreeLearn is a deep learning approach for tree instance segmentation of forest point clouds.<n>TreeLearn is trained on already segmented point clouds in a data-driven manner.<n>We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software.
  arXiv  Detail & Related papers  (2023-09-15T15:20:16Z)
• FOR-instance: a UAV laser scanning benchmark dataset for semantic and instance segmentation of individual trees [0.06597195879147556]
  FOR-instance dataset comprises five curated and ML-ready UAV-based laser scanning data collections. The dataset is divided into development and test subsets, enabling method advancement and evaluation. The inclusion of diameter at breast height data expands its utility to the measurement of a classic tree variable.
  arXiv  Detail & Related papers  (2023-09-03T22:08:29Z)
• A Survey of Label-Efficient Deep Learning for 3D Point Clouds [109.07889215814589]
  This paper presents the first comprehensive survey of label-efficient learning of point clouds. We propose a taxonomy that organizes label-efficient learning methods based on the data prerequisites provided by different types of labels. For each approach, we outline the problem setup and provide an extensive literature review that showcases relevant progress and challenges.
  arXiv  Detail & Related papers  (2023-05-31T12:54:51Z)
• A Trainable Optimal Transport Embedding for Feature Aggregation and its Relationship to Attention [96.77554122595578]
  We introduce a parametrized representation of fixed size, which embeds and then aggregates elements from a given input set according to the optimal transport plan between the set and a trainable reference. Our approach scales to large datasets and allows end-to-end training of the reference, while also providing a simple unsupervised learning mechanism with small computational cost.
  arXiv  Detail & Related papers  (2020-06-22T08:35:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.