A new SmartForest publication is out as a collaboration with ETH Zürich on Automated forest inventory: Analysis of high-density airborne LiDAR pointclouds with 3D deeplearning !
Congratualations to the authors binbin xiang, Maciej Wielgosz, Theodora Kontogianni, Torben Peters, Stefano Puliti, Rasmus Astrup, Konrad Schindler
The highlights:
🔦 The authors propose a deep learning framework for multiple segmentation tasks in forests.
🔦 The segmentation includes semantic and instance segmentation in forest scenes.
🔦 The state-of-art individual tree segmentation is tested on a public forest dataset.
🔦 Biophysical parameters of individual trees as well as stands are predicted well.
🔦 Algorithm is designed for high-density 3D airborne laser scanning point cloud data.
read the full article here: https://www.sciencedirect.com/science/article/pii/S0034425724000890