… machine learning algorithms such as random forest algorithm and the decision tree algorithm
[25], this study used deep learning network to establish the pine wilt disease dead tree …

… With the advent of Machine Learning, Deep Learning has proven to successfully achieve …
R-CNN Deep Learning approach for detecting and segmenting standing dead trees in a mixed …

… The precision accuracy is the probability that a detected dead tree is in fact a valid dead
tree. The recall accuracy is the probability that a dead tree that exists in the ground truth is …

… difficult to detect standing dead trees (SDTs) … dead tree extent in a confidence map, and by
following this with maximum location searching we can easily locate the individual dead trees. …

… trees’ geometric structure, as well as trees beneath the upper canopy layer. In this paper, the
potential of two deep learning … classification of four different tree classes is evaluated using …

… study was to use DL to identify those dead trees. In the … dead trees in forests using UAV
orthophotos. To summarize, this master's thesis presents semantic segmentation of dead trees (…

… Deep learning-based approaches … 3D single tree objects with respect to tree species and
dead trees in a … Additionally, we applied the technique of transfer learning to tree species …

… Deep convolutional neural networks (CNNs) are deep learning models which have …
Since highly defoliated trees are spectrally very similar to dead trees, we defined that a 50% …

… dead trees after pest infestation is becoming increasingly important. New deep learning …
multiple tree species (pine, birch, alder) and standing dead trees with crowns using the 3D …

… or completely dead tree missing a crown or most of the smaller branches, whereas trees
labeled “dead tree” are dead trees with crowns. The distinction between “snag” and “dead tree” …