Training point-based deep learning networks for forest segmentation with synthetic data

arXiv:2403.14115v1 Announce Type: new
Abstract: Remote sensing through unmanned aerial systems (UAS) has been increasing in forestry in recent years, along with using machine learning for data processing. Deep learning architectures, extensively applied in natural language and image processing, have recently been extended to the point cloud domain. However, the availability of point cloud datasets for training and testing remains limited. Creating forested environment point cloud datasets is expensive, requires high-precision sensors, and is time-consuming as manual point classification is …

abstract aerial architectures arxiv cloud cs.cv data data processing deep learning domain however image image processing language machine machine learning natural natural language networks processing segmentation sensing synthetic synthetic data systems through training type