Coniferous Forest

Coniferous forest research currently focuses on developing advanced methods for automated data acquisition and analysis, particularly using remote sensing technologies like lidar and satellite imagery. This involves employing deep learning architectures, such as U-Net and PointNet++, for tasks like tree species classification, canopy height mapping, and terrain classification, often incorporating optimization techniques to improve model performance. These efforts aim to improve forest management practices, enabling more efficient resource monitoring and sustainable harvesting, while also advancing the capabilities of autonomous navigation systems in challenging off-road environments.