Individual Tree Crown Detection and Classification of Live and Dead Trees Using a Mask Region-Based Convolutional Neural Network (Mask R-CNN) DOI Open Access
Shilong Yao, Zhenbang Hao,

Christopher J. Post

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1900 - 1900

Published: Oct. 28, 2024

Mapping the distribution of living and dead trees in forests, particularly ecologically fragile areas where forests serve as crucial ecological environments, is essential for assessing forest health, carbon storage capacity, biodiversity. Convolutional neural networks, including Mask R-CNN, can assist rapid accurate monitoring. In this study, R-CNN was employed to detect crowns Casuarina equisetifolia distinguish between live Pingtan Comprehensive Pilot Zone, Fujian, China. High-resolution images five plots were obtained using a multispectral Unmanned Aerial Vehicle. Six band combinations derivatives, RGB, RGB-digital surface model (DSM), Multispectral, Multispectral-DSM, Vegetation Index, Vegetation-Index-DSM, used tree crown detection classification trees. Five-fold cross-validation divide manually annotated dataset 21,800 7157 into training validation sets, which validating models. The results demonstrate that RGB combination achieved most effective performance (average F1 score = 74.75%, IoU 70.85%). RGB–DSM exhibited highest accuracy 71.16%, 68.28%). lower than trees, may be due similar spectral features across similarity background, resulting false identification. For simultaneous produced promising 74.18%, 69.8%). It demonstrates achieve Our study could provide managers with detailed information on condition, has potential improve management.

Language: Английский

Canopy extraction of mango trees in hilly and plain orchards using UAV images: Performance of machine learning vs deep learning DOI Creative Commons

Yuqi Yang,

Tiwei Zeng, Long Li

et al.

Ecological Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 103101 - 103101

Published: March 1, 2025

Language: Английский

Citations

0
Research on the segmentation of individual trees and the extraction of structural parameters in eucalyptus plantations based on a TEMA mask R-CNN model DOI

Runlian Huang,

Jirong Ding,

Zhaotong Ren

et al.

Journal of Forest Research, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 12

Published: March 17, 2025

Language: Английский

Citations

0
Harnessing AI for Precise AGB Estimation in Remote Areas DOI
Fajar W. Wijaya,

Mikael Prakoso,

Muhammad Bondan V. Ramadhan

et al.

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 99 - 132

Published: Feb. 28, 2025

Renewable energy, particularly biomass, plays a vital role in addressing global energy needs and mitigating climate change. The development of sustainable accurate biomass estimation methods is crucial to meet these demands. This chapter explores the transformative impact advancements computer vision (CV) technologies on above-ground (AGB) estimation, focusing both ground-based aerial remote sensing techniques. provides comprehensive overview CV applications AGB including use UAVs, smartphones, LiDAR for capturing forest structural parameters. Despite challenges related dataset variability, model complexity, logistical constraints environments, this discusses recent trends methodologies that address challenges. concludes with discussion open research issues future recommendations advancing using CV, aimed at supporting informed decision-making conservation change mitigation strategies.

Language: Английский

Citations

0
Research on eucalyptus individual tree segmentation and age estimation utilizing improved Mask R-CNN algorithm based on UAV stereo images DOI Creative Commons

Jirong Ding,

Li-Bi You,

Yehua Liang

et al.

Industrial Crops and Products, Journal Year: 2025, Volume and Issue: 230, P. 121073 - 121073

Published: April 23, 2025

Language: Английский

Citations

0
Two-Stage Deep Learning Framework for Individual Tree Crown Detection and Delineation in Mixed-Wood Forests Using High-Resolution Light Detection and Ranging Data DOI Creative Commons
Qian Li, Baoxin Hu, Jiali Shang

et al.

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(9), P. 1578 - 1578

Published: April 29, 2025

Accurate detection and delineation of individual tree crowns (ITCs) are essential for sustainable forest management ecosystem monitoring, providing key biophysical attributes at the level. However, complex structure mixed-wood forests, characterized by overlapping canopies various shapes sizes, presents significant challenges, often compromising accuracy. This study a two-stage deep learning framework that integrates Canopy Height Model (CHM)-based treetop with three-dimensional (3D) ITC using high-resolution airborne LiDAR point cloud data. In first stage, Mask R-CNN detects treetops from CHM, precise initial localizations trees. second 3D U-Net architecture clusters points to delineate boundaries in space. Evaluated against manually delineated reference data, our approach outperforms established methods, including alone lidR itcSegment algorithm, achieving mean intersection-over-union (mIoU) scores 0.82 coniferous plots, 0.81 0.79 deciduous plots. demonstrates great potential as robust solution forests.

Language: Английский

Citations

0
A Driving Warning System for Explosive Transport Vehicles Based on Object Detection Algorithm DOI Creative Commons
Jinshan Sun,

Ronghuan Zheng,

Xuan Liu

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(19), P. 6339 - 6339

Published: Sept. 30, 2024

Due to the flammable and explosive nature of explosives, there are significant potential hazards risks during transportation. During operation transport vehicles, often situations where vehicles around them approach or change lanes abnormally, resulting in insufficient avoidance collision, leading serious consequences such as explosions fires. Therefore, response above issues, this article has developed an vehicle driving warning system based on object detection algorithms. Consumer-level cameras flexibly arranged body monitor surrounding vehicles. Using YOLOv4 algorithm identify distance using a game theory-based cellular automaton model simulate actual simulating driver’s decision-making behavior when encountering other approaching changing abnormally driving. The was used two scenarios equipped with without systems. results show that encounter above-mentioned dangerous situations, can timely issue warnings, remind drivers make decisions, avoid risks, ensure safety operation, verify effectiveness system.

Language: Английский

Citations

2
A Mixed Broadleaf Forest Segmentation Algorithm Based on Memory and Convolution Attention Mechanisms DOI Open Access

Xing Tang,

Zheng Li,

Wenfei Zhao

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(8), P. 1310 - 1310

Published: July 26, 2024

Counting the number of trees and obtaining information on tree crowns have always played important roles in efficient high-precision monitoring forest resources. However, determining how to obtain above at a low cost with high accuracy has been topic great concern. Using deep learning methods segment individual mixed broadleaf forests is cost-effective approach resource assessment. Existing crown segmentation algorithms primarily focus discrete trees, limited research forests. The lack datasets resulted poor performance, occlusions images hinder accurate segmentation. To address these challenges, this study proposes supervised method, SegcaNet, which can efficiently extract from UAV under natural light conditions. A dataset for dense produced, containing 18,000 single-tree 1200 images. SegcaNet achieves superior results by incorporating convolutional attention mechanism memory module. experimental indicate that SegcaNet’s mIoU values surpass those traditional algorithms. Compared FCN, Deeplabv3, MemoryNetV2, increased 4.8%, 4.33%, 2.13%, respectively. Additionally, it reduces instances incorrect over-segmentation.

Language: Английский

Citations

0
A comprehensive review on tree detection methods using point cloud and aerial imagery from unmanned aerial vehicles DOI

Weijie Kuang,

Hann Woei Ho, Ye Zhou

et al.

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 227, P. 109476 - 109476

Published: Oct. 1, 2024

Language: Английский

Citations

0
Estimation of Tree Diameter at Breast Height from Aerial Photographs Using a Mask R-CNN and Bayesian Regression DOI Open Access
K. H. Kwon, Seong-kyun Im, Sung‐Yong Kim

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1881 - 1881

Published: Oct. 25, 2024

A probabilistic estimation model for forest biomass using unmanned aerial vehicle (UAV) photography was developed. We utilized a machine-learning-based object detection algorithm, mask region-based convolutional neural network (Mask R-CNN), to detect trees in photographs. Subsequently, Bayesian regression used calibrate the based on an allometric estimated crown diameter (CD) obtained from photographs and analyzed at breast height (DBH) data acquired through terrestrial laser scanning. The F1 score of Mask R-CNN individual tree 0.927. Moreover, CD acceptable (rRMSE = 10.17%). Accordingly, DBH successfully calibrated regression. predictive distribution accurately predicted validation data, with 98.6% 56.7% being within 95% 50% prediction intervals, respectively. Furthermore, uncertainty more practical reliable compared traditional ordinary least squares (OLS). Our can be applied estimate level. Particularly, approach this study provides benefit risk assessments. Additionally, since workflow is not interfered by canopy, it effectively dense canopy conditions.

Language: Английский

Citations

0
Individual Tree Crown Detection and Classification of Live and Dead Trees Using a Mask Region-Based Convolutional Neural Network (Mask R-CNN) DOI Open Access
Shilong Yao, Zhenbang Hao,

Christopher J. Post

et al.

Forests, Journal Year: 2024, Volume and Issue: 15(11), P. 1900 - 1900

Published: Oct. 28, 2024

Mapping the distribution of living and dead trees in forests, particularly ecologically fragile areas where forests serve as crucial ecological environments, is essential for assessing forest health, carbon storage capacity, biodiversity. Convolutional neural networks, including Mask R-CNN, can assist rapid accurate monitoring. In this study, R-CNN was employed to detect crowns Casuarina equisetifolia distinguish between live Pingtan Comprehensive Pilot Zone, Fujian, China. High-resolution images five plots were obtained using a multispectral Unmanned Aerial Vehicle. Six band combinations derivatives, RGB, RGB-digital surface model (DSM), Multispectral, Multispectral-DSM, Vegetation Index, Vegetation-Index-DSM, used tree crown detection classification trees. Five-fold cross-validation divide manually annotated dataset 21,800 7157 into training validation sets, which validating models. The results demonstrate that RGB combination achieved most effective performance (average F1 score = 74.75%, IoU 70.85%). RGB–DSM exhibited highest accuracy 71.16%, 68.28%). lower than trees, may be due similar spectral features across similarity background, resulting false identification. For simultaneous produced promising 74.18%, 69.8%). It demonstrates achieve Our study could provide managers with detailed information on condition, has potential improve management.

Language: Английский

Citations

0