CEDAnet: Individual Tree Segmentation in Dense Orchard via Context Enhancement and Density Prior
Individual tree segmentation (ITS) is a pivotal technique in orchard research, estimating tree counts and delineating crown contours. This method provides foundational data for assessing orchard health, nutritional composition, and predicting yield. Unmanned aerial vehicles (UAVs) have become an essential data source for (ITS) due to their capability ...
Fangjie Zhu +4 more
openaire +3 more sources
ForestSeg3D: A semantically guided framework for individual tree segmentation from LiDAR point clouds. [PDF]
Zhang K, Zhang W, Jing W, Li L, Li C.
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A Bottom-Up Multi-Feature Fusion Algorithm for Individual Tree Segmentation in Dense Rubber Tree Plantations Using Unmanned Aerial Vehicle-Light Detecting and Ranging. [PDF]
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Toward Consistent Canopy Characterization From Low-Cost UAV Imagery. [PDF]
Plumacker A +16 more
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From Detection to Maps: A Review of Automated Urban Tree Mapping Using UAV and High-Resolution Satellite Data. [PDF]
Satbayev S +4 more
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LiDAR-derived dataset of extracted trees and shrubs from AHN4 airborne laser scanning in the Oostvaardersplassen wetland, the Netherlands. [PDF]
Wang J, Kissling WD.
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Fusion of MLP, XGBoost, and QAT-Optimized PointNet++ for Predicting Short-Term Dendrometer-Derived Stem Dynamics: An Edge-Oriented Computational Framework. [PDF]
Bolikulov F +8 more
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Scaling Up Forest Vision with Synthetic Data. [PDF]
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Segmentation and morphological analysis of recanalized coiled aneurysms: Towards an automatic tool for clipping planning in microsurgery. [PDF]
Coupet M +9 more
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