Recent advances in the application of deep learning methods to forestry

Cross-site learning in deep learning RGB tree crown detection

(2020) Ben G. Weinstein et al.   Ecological Informatics

Increasing the robustness of material-specific deep learning models for crack detection across different materials

(2020) Mohamad Alipour et al.   ENGINEERING STRUCTURES

Prediction of moisture content of wood using Modified Random Frog and Vis-NIR hyperspectral imaging

(2020) Jianyu Chen et al.   INFRARED PHYSICS & TECHNOLOGY

Sustainable Forest Management Beyond the Timber-Oriented Status Quo: Transitioning to Co-production of Timber and Non-wood Forest Products—a Global Perspective

(2020) Jonathan P. Sheppard et al.   Current Forestry Reports

Non-parametric learning of lifted Restricted Boltzmann Machines

(2020) Navdeep Kaur et al.   INTERNATIONAL JOURNAL OF APPROXIMATE REASONING

Theory of deep convolutional neural networks: Downsampling

(2020) Ding-Xuan Zhou   NEURAL NETWORKS

A Brief Survey on Semantic Segmentation with Deep Learning

(2020) Shijie Hao et al.   NEUROCOMPUTING

Text alignment in early printed books combining deep learning and dynamic programming

(2020) Zahra Ziran et al.   PATTERN RECOGNITION LETTERS

Library of deep-learning image segmentation and outcomes model-implementations

(2020) Aditya P. Apte et al.   Physica Medica-European Journal of Medical Physics

Deep learning in environmental remote sensing: Achievements and challenges

(2020) Qiangqiang Yuan et al.   REMOTE SENSING OF ENVIRONMENT

Wood Density and Moisture Content Estimation by Drilling Chips Extraction Technique

(2020) Roberto D. Martínez et al.   Materials

Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier

(2020) Gensheng Hu et al.   BIOSYSTEMS ENGINEERING

Extracting apple tree crown information from remote imagery using deep learning

(2020) Jintao Wu et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Sleep staging by bidirectional long short-term memory convolution neural network

(2020) Xueyan Chen et al.   Future Generation Computer Systems-The International Journal of eScience

Deep multi-metric learning for text-independent speaker verification

(2020) Jiwei Xu et al.   NEUROCOMPUTING

Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm

(2020) Fenglong Ding et al.   SENSORS

A review of multi-objective deep learning speech denoising methods

(2020) Arian Azarang et al.   SPEECH COMMUNICATION

Deep learning based emotion analysis of microblog texts

(2020) Dongliang Xu et al.   Information Fusion

A 3D model for knots and related fiber deviations in sawn timber for prediction of mechanical properties of boards

(2019) Markus Lukacevic et al.   MATERIALS & DESIGN

Deep learning for use in lumber classification tasks

(2019) Junfeng Hu et al.   WOOD SCIENCE AND TECHNOLOGY

An Analytical Model for Rank Distribution in Sparse Network Coding

(2019) Hadi Sehat et al.   IEEE COMMUNICATIONS LETTERS

Discriminant Deep Feature Learning based on joint supervision Loss and Multi-layer Feature Fusion for heterogeneous face recognition

(2019) Weipeng Hu et al.   COMPUTER VISION AND IMAGE UNDERSTANDING

Out-of-grade sawn pine: A state-of-the-art review on challenges and new opportunities in cross laminated timber (CLT)

(2019) Rebecca Cherry et al.   CONSTRUCTION AND BUILDING MATERIALS

Deep learning in remote sensing applications: A meta-analysis and review

(2019) Lei Ma et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

TA-CNN: Two-way attention models in deep convolutional neural network for plant recognition

(2019) Youxiang Zhu et al.   NEUROCOMPUTING

Internet of Things to network smart devices for ecosystem monitoring

(2019) Xin Li et al.   Science Bulletin

Online Monitoring of Welding Status Based on a DBN Model During Laser Welding

(2019) Yanxi Zhang et al.   Engineering

Projecting Australia's forest cover dynamics and exploring influential factors using deep learning

(2019) Long Ye et al.   ENVIRONMENTAL MODELLING & SOFTWARE

A review of remote sensing for mangrove forests: 1956–2018

(2019) Le Wang et al.   REMOTE SENSING OF ENVIRONMENT

Classification of tree species and stock volume estimation in ground forest images using Deep Learning

(2019) Jiazheng Liu et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Enhanced Stacked Denoising Autoencoder-Based Feature Learning for Recognition of Wafer Map Defects

(2019) Jianbo Yu   IEEE TRANSACTIONS ON SEMICONDUCTOR MANUFACTURING

Deep learning in video multi-object tracking: A survey

(2019) Gioele Ciaparrone et al.   NEUROCOMPUTING

Automated Identification of Wood Veneer Surface Defects Using Faster Region-Based Convolutional Neural Network with Data Augmentation and Transfer Learning

(2019) Augustas Urbonas et al.   Applied Sciences-Basel

Unmanned Aerial Vehicle (UAV) based Forest Fire Detection and monitoring for reducing false alarms in forest-fires

(2019) S. Sudhakar et al.   COMPUTER COMMUNICATIONS

Real-time forest smoke detection using hand-designed features and deep learning

(2019) Yingshu Peng et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Deep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees

(2019) Hamid Hamraz et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Superpixel-enhanced deep neural forest for remote sensing image semantic segmentation

(2019) Li Mi et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Application of deep convolutional neural network on feature extraction and detection of wood defects

(2019) Ting He et al.   MEASUREMENT

A sparse deep belief network with efficient fuzzy learning framework

(2019) Gongming Wang et al.   NEURAL NETWORKS

Towards an integrated research approach for urban forestry: The case of China

(2019) Nancai Pei et al.   URBAN FORESTRY & URBAN GREENING

The Deep Kernelized Autoencoder

(2018) Michael Kampffmeyer et al.   APPLIED SOFT COMPUTING

Deep learning in agriculture: A survey

(2018) Andreas Kamilaris et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

A Fuzzy Restricted Boltzmann Machine: Novel Learning Algorithms Based on the Crisp Possibilistic Mean Value of Fuzzy Numbers

(2018) Shuang Feng et al.   IEEE TRANSACTIONS ON FUZZY SYSTEMS

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

(2018) Liang-Chieh Chen et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Important LiDAR metrics for discriminating forest tree species in Central Europe

(2018) Yifang Shi et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Automatic Tree Identification and Diameter Estimation Using Single Scan Terrestrial Laser Scanner Data in Central Indian Forests

(2018) R. Suraj Reddy et al.   Journal of the Indian Society of Remote Sensing

Alignment-consistent recursive neural networks for bilingual phrase embeddings

(2018) Jinsong Su et al.   KNOWLEDGE-BASED SYSTEMS

Plant identification based on very deep convolutional neural networks

(2018) Heyan Zhu et al.   MULTIMEDIA TOOLS AND APPLICATIONS

An overview on Restricted Boltzmann Machines

(2018) Nan Zhang et al.   NEUROCOMPUTING

Study of the quality of wood texture patterns in digital image correlation

(2018) Xiaomao Zhao et al.   OPTIK

A novel image segmentation approach for wood plate surface defect classification through convex optimization

(2018) Zhanyuan Chang et al.   JOURNAL OF FORESTRY RESEARCH

The Vapnik–Chervonenkis dimension of graph and recursive neural networks

(2018) Franco Scarselli et al.   NEURAL NETWORKS

Graph structured autoencoder

(2018) Angshul Majumdar   NEURAL NETWORKS

Prediction of equilibrium moisture content and specific gravity of heat treated wood by artificial neural networks

(2017) Sukru Ozsahin et al.   European Journal of Wood and Wood Products

Deep learning for biological image classification

(2017) Carlos Affonso et al.   EXPERT SYSTEMS WITH APPLICATIONS

Fully Convolutional Networks for Semantic Segmentation

(2017) Evan Shelhamer et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

(2017) Shaoqing Ren et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

A deep learning method for classifying mammographic breast density categories

(2017) Aly A. Mohamed et al.   MEDICAL PHYSICS

G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition

(2017) Pengjie Tang et al.   NEUROCOMPUTING

Wood moisture content prediction using feature selection techniques and a kernel method

(2017) Hela Daassi-Gnaba et al.   NEUROCOMPUTING

A survey of deep neural network architectures and their applications

(2017) Weibo Liu et al.   NEUROCOMPUTING

A new prediction-based variance estimator for two-stage model-assisted surveys of forest resources

(2017) Svetlana Saarela et al.   REMOTE SENSING OF ENVIRONMENT

Moisture content prediction of rain-exposed wood: Test and evaluation of a simple numerical model for durability applications

(2016) J. Niklewski et al.   BUILDING AND ENVIRONMENT

Neural networks: An overview of early research, current frameworks and new challenges

(2016) Alberto Prieto et al.   NEUROCOMPUTING

Review of studies on tree species classification from remotely sensed data

(2016) Fabian Ewald Fassnacht et al.   REMOTE SENSING OF ENVIRONMENT

Biophysical climate impacts of recent changes in global forest cover

(2016) R. Alkama et al.   SCIENCE

Semi-Supervised Text Classification With Universum Learning

(2016) Chien-Liang Liu et al.   IEEE Transactions on Cybernetics

Optimisation in Forest Management

(2016) Abdulğaffar Kaya et al.   Current Forestry Reports

In-situ assessment of timber structural members: Combining information from visual strength grading and NDT/SDT methods – A review

(2015) Artur Feio et al.   CONSTRUCTION AND BUILDING MATERIALS

Hyperspectral Tree Species Classification of Japanese Complex Mixed Forest With the Aid of Lidar Data

(2015) Tomohiro Matsuki et al.   IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Automatic Feature Learning to Grade Nuclear Cataracts Based on Deep Learning

(2015) Xinting Gao et al.   IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING

Fuzzy Restricted Boltzmann Machine for the Enhancement of Deep Learning

(2015) C. L. Philip Chen et al.   IEEE TRANSACTIONS ON FUZZY SYSTEMS

Semi-supervised SVM for individual tree crown species classification

(2015) Michele Dalponte et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

Deep learning

(2015) Yann LeCun et al.   NATURE

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

(2015) Babak Alipanahi et al.   NATURE BIOTECHNOLOGY

Study on the identification of the wood surface defects based on texture features

(2015) Xie YongHua et al.   OPTIK

A critical synthesis of remotely sensed optical image change detection techniques

(2015) Andrew P. Tewkesbury et al.   REMOTE SENSING OF ENVIRONMENT

Wood defect detection method with PCA feature fusion and compressed sensing

(2015) Yizhuo Zhang et al.   JOURNAL OF FORESTRY RESEARCH

Remote Sensing Concepts and Their Applicability in REDD+ Monitoring

(2015) Timo Tokola   Current Forestry Reports

Urban tree species mapping using hyperspectral and lidar data fusion

(2014) Michael Alonzo et al.   REMOTE SENSING OF ENVIRONMENT

Sparse Extreme Learning Machine for Classification

(2014) Zuo Bai et al.   IEEE Transactions on Cybernetics

Efficiency of visual strength grading of timber with respect to origin, species, cross section, and grading rules: a critical evaluation of the common standards

(2013) Peter Stapel et al.   HOLZFORSCHUNG

Tree Diversity Detection with Mid-Resolution Images and Environmental Data in a Neural Network

(2013) Mohammad Redowan   Journal of the Indian Society of Remote Sensing

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

(2012) F. Longuetaud et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

Pattern recognition and size determination of internal wood defects based on wavelet neural networks

(2009) Lihai Wang et al.   COMPUTERS AND ELECTRONICS IN AGRICULTURE

A Survey on Transfer Learning

(2009) Sinno Jialin Pan et al.   IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING

Automated visual inspection system for wood defect classification using computational intelligence techniques

(2009) Gonzalo A. Ruz et al.   INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE

Wood defect classification based on image analysis and support vector machines

(2009) Irene Yu-Hua Gu et al.   WOOD SCIENCE AND TECHNOLOGY