Label-Efficient 3D Forest Mapping: Self-Supervised and Transfer Learning for Instance Segmentation, Semantic Segmentation, and Species Classification
Helmholtz-Zentrum Dresden-Rossendorf (HZDR), Helmholtz Institute Freiberg for Resource Technology (HIF)（德累斯顿-罗斯托克亥姆霍兹中心（HZDR）、弗里贝格资源技术亥姆霍兹研究所（HIF））；Remote Sensing and Geoinformatics, Freie Universität Berlin（柏林自由大学遥感与地理信息学系）；Institute of Geomatics, BOKU University（博科尼大学测绘学院）；Faculty of Electrical and Computer Engineering, University of Iceland（爱沙尼亚大学电气与计算机工程学院）
Aldino Rizaldy, Fabian Ewald Fassnacht, Ahmed Jamal Afifi, Hua Jiang, Richard Gloaguen, Pedram Ghamisi
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. Our objective is to improve performance across three tasks: instance segmentation, semantic segmentation, and tree classification using realistic and operational training sets. We observe improvements across all tasks, compared to training from scratch, evaluated with their respective metrics. For instance segmentation, self-supervised learning combined with domain adaptation improves AP50 by 16.98%. For semantic segmentation, self-supervised learning alone improves mIoU by 1.79%. For tree classification, hierarchical transfer learning improves mean Jaccard by 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.
Integrating semi-supervised label propagation and random forests for multi-atlas based hippocampus segmentation
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；School of Computer and Control Engineering, Yantai University（烟台大学计算机与控制工程学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Point2Tree(P2T) -- framework for parameter tuning of semantic and instance segmentation used with mobile laser scanning data in coniferous forest
Norwegian Institute for Bioeconomy Research (NIBIO)（挪威生物经济研究所（NIBIO））；University College London（伦敦大学学院）；NERC National Centre for Earth Observation（NERC国家地球观测中心）
Multi-task fully convolutional network for tree species mapping in dense forests using small training hyperspectral data
Pontifical Catholic University of Rio de Janeiro（里约热内卢天主教大学）；IBM Research（IBM研究院）；McMaster University（麦克马斯特大学）；National Institute for Space Research (INPE)（巴西国家空间研究所）；Santa Catarina State University (UDESC)（圣卡塔琳娜州立大学）
SelectAnyTree: A Promptable Instance Segmentation Model for 3D Forest LiDAR Point Clouds
Nagoya University（名古屋大学）；RIKEN（理化学研究所）；University of Freiburg（弗莱堡大学）；University of California, Los Angeles（加州大学洛杉矶分校）；University of Twente（特温特大学）；KC Machine Learning Lab（KC机器学习实验室）；Hanoi University of Science and Technology（河内科技大学）；Ritsumeikan University（立命馆大学）
Semantic segmentation of forest stands using deep learning
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences（生命科学大学环境科学与自然资源管理学院）；Faculty of Science and Technology, Norwegian University of Life Sciences（生命科学大学科学与技术学院）
3D forest semantic segmentation using multispectral LiDAR and 3D deep learning
Bruno Kessler Foundation (FBK)（布鲁诺·凯塞勒基金会）；TU Wien（维也纳工业大学）；Finnish Geospatial Research Institute（芬兰地理空间研究所）；National Land Survey of Finland（芬兰国家土地测量局）
LeafInst - Unified Instance Segmentation Network for Fine-Grained Forestry Leaf Phenotype Analysis: A New UAV based Benchmark
organization= College of Information Science；Technology \& Artificial Intelligence, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= Department of Geography, College of Natural Resources；organization= Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University , city= Nanjing , postcode= 210037 , state= Jiangsu , country= China；organization= School of Architecture \& Urban Planning, Shenzhen University , city= Shenzhen , postcode= 518060 , state= Guangdong , country= China；organization= School of Software, Hefei University of Technology , city= Hefei , postcode= 230601 , state= Anhui , country= China
SilvaScenes: Tree Detection and Species Classification from Under-Canopy Images in Natural Forests
Northern Robotics Laboratory, Université Laval（北方机器人实验室，拉瓦尔大学）；Département des sciences du bois et de la forêt, Université Laval（林业与木材科学系，拉瓦尔大学）；Institut des Sciences de la Forêt tempérée, Université du Québec en Outaouais（温带森林科学研究所，魁北克outsouais大学）
H-EMD: A Hierarchical Earth Mover's Distance Method for Instance Segmentation
University of Notre Dame（圣母大学）；Nanjing University of Science and Technology（南京理工大学）；Leibniz-Institut für Analytische Wissenschaften–ISAS–e.V.（莱布尼茨分析科学研究所）
Monitoring Urban Forests from Auto-Generated Segmentation Maps
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；Technical University of Munich（慕尼黑工业大学）；TJ Watson Research Center, IBM Research（IBM研究院托马斯·J·沃森研究中心）
Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery
DeepForest Technologies Co., Ltd.（深森林技术有限公司）；YM Lab.（YM实验室）；Graduate School of Agriculture, Kyoto University（京都大学农业研究院）；Graduate School of Science, Osaka Metropolitan University（大阪 metropolitan 大学理学研究院）；Faculty of Tropical Forestry, Universiti Malaysia Sabah（马来西亚沙巴大学热带林业学院）；Forest Research Centre, Sabah Forestry Department（沙巴林业部门森林研究中心）
3D Adapted Random Forest Vision (3DARFV) for Untangling Heterogeneous-Fabric Exceeding Deep Learning Semantic Segmentation Efficiency at the Utmost Accuracy
Unsupervised deep learning for semantic segmentation of multispectral LiDAR forest point clouds
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, The National Land Survey of Finland（遥感与摄影测量系，芬兰地理研究 institute FGI，芬兰国家土地测绘局）；D Optical Metrology (3DOM) unit, Bruno Kessler Foundation (FBK)（3D光学计量（3DOM）单元，布鲁诺·凯瑟尔基金会（FBK））；Department of Built Environment, Aalto University, School of Engineering（环境学院，艾尔托大学，工程学院）
treeX: Unsupervised Tree Instance Segmentation in Dense Forest Point Clouds
University of Potsdam（波恩大学）；Digital Engineering Faculty（数字工程学院）；BOKU University（博科尼大学）；Department of Ecosystem Management, Climate and Biodiversity（生态系统管理、气候与生物多样性部门）；Institute of Forest Growth（森林生长研究所）；Eberswalde University of Sustainable Development（埃伯斯瓦尔德可持续发展大学）；State Forestry Research Centre（州林业研究机构）；Hasso Plattner Institute for Digital Engineering（哈索·plattnner数字工程研究所）
Static Segmentation by Tracking: A Label-Efficient Approach for Fine-Grained Specimen Image Segmentation
The Ohio State University（俄亥俄州立大学）；Princeton University（普林斯顿大学）；University of Maine（缅因大学）；University of Miami（迈阿密大学）；Virginia Tech（弗吉尼亚理工大学）；Duke University（杜克大学）；Rensselaer Polytechnic Institute（拉特格斯理工学院）
Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: a possible contribution for updating forest management plans
University of Padova（帕多瓦大学）；Fondazione Edmund Mach（埃德蒙·马赫基金会）；Provincia Autonoma di Trento（特伦托自治省）
