A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN
Pinus pinaster, commonly called the maritime pine, is a vital species in Mediterranean forests. Its ability to thrive in the local climate and rapid growth make it an essential resource for wood production and reforestation efforts. Accurately estimating the volume of wood within a pine forest is of great significance to the wood industry. The traditional process is either a rough estimation without measurements or a time-consuming process based on manual measurements and calculations. This article presents a method for determining a tree’s diameter, total height, and volume based on a photograph. The method involves placing reference targets of known dimensions on the trees. A deep learning neural network is used to extract the tree trunk and the targets from the background, and the dimensions of the trunk are estimated based on the dimensions of the targets. The results indicate less than 10% estimation errors for diameter, height, and volume in general. The proposed methodology automates the estimation of the dendrometric characteristics of trees, reducing field time consumed in a forest inventory and without the need to use nonprofessional instruments.
Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN
Pinus pinaster, commonly called the maritime pine, is a vital species in Mediterranean forests. Its ability to thrive in the local climate and rapid growth make it an essential resource for wood production and reforestation efforts. Accurately estimating the volume of wood within a pine forest is of great significance to the wood industry. The traditional process is either a rough estimation without measurements or a time-consuming process based on manual measurements and calculations. This article presents a method for determining a tree’s diameter, total height, and volume based on a photograph. The method involves placing reference targets of known dimensions on the trees. A deep learning neural network is used to extract the tree trunk and the targets from the background, and the dimensions of the trunk are estimated based on the dimensions of the targets. The results indicate less than 10% estimation errors for diameter, height, and volume in general. The proposed methodology automates the estimation of the dendrometric characteristics of trees, reducing field time consumed in a forest inventory and without the need to use nonprofessional instruments.
Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN
Ana Malta (author) / José Lopes (author) / Raúl Salas-González (author) / Beatriz Fidalgo (author) / Torres Farinha (author) / Mateus Mendes (author)
2023
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
| BASE | 2018
Mode II Fracture Characterization of Pinus Pinaster Wood
| British Library Online Contents | 2008
Improvement of Pinus pinaster wood dimensional stability using cork industry wastes
| British Library Online Contents | 1998