A research led by Associate Professor Lu Hao from Beijing Forestry University's School of Information Science and Technology has developed Sen-net, an AI-powered 3D point cloud understanding framework for forest ecosystems, published in Remote Sensing of Environment (IF=11.1) in the title of "Towards a point cloud understanding framework for forest scene semantic segmentation across forest types and sensor platforms". The study, conducted with China's Academy of Forestry and Switzerland's WSL Institute, introduces a cross-platform solution for accurate vegetation classification across diverse forest types.

Foliage, wood (i.e., trunk, branch), ground, and lower objects (i.e., grass, shrubs), are key semantic components of forests that play different roles in the forest ecosystem. For understanding forest ecosystem structure and function, Light Detection And Ranging (LiDAR) point cloud is a valuable form of remote sensing observation. The understanding intensively relies on precisely performing semantic segmentation task to segment semantic components from forest point cloud data. However, the semantic segmentation of massive point cloud data from forest scenes remains a significant challenge. The forest environment is highly heterogeneous and complex due to tree species and terrain conditions. Different climate zones lead to varying canopy characteristics and the diversity of LiDAR platforms delivers inconsistent point cloud properties. Heuristic approaches and conventional machine learning approaches inevitably suffer from poor generalization. Additionally, most deep learning methods lack a dedicated network design to address the characteristics of forests.

This paper introduces Sen-net, a point cloud understanding network specifically constructed for semantic component segmentation of forest scene point cloud. Sen-net implements three modules tailored for forest characteristics. First, a spatial context enhancement module (SCEM) is designed for providing both global and dataset-level perspectives to mine geometric information and robust features hidden in heterogeneous forest. Second, a semantic-driven detail enrichment module (SDEM) is incorporated to preserve rich geometric details and semantic information thereby enhancing the learning of complex structures in the forests. Finally, an adaptive guidance flow (AGF) is added to seamlessly fuse the semantic and detailed features. Comprehensive experiments were conducted on both self-built Lin3D dataset and public datasets. Sen-net achieved an OA of 97.6 % and 85.1 % MIoU on the Lin3D dataset, and an OA of 94.5 % and 78.2 % MIoU on the public dataset FOR-instance.

Results show that Sen-net outperformed the representative forest scene point cloud semantic segmentation approaches and state-of-the-art deep learning networks, and it has the potential to generalize to point cloud data collected by LiDAR from other platforms. It is concluded that Sen-net is a powerful and robust framework with substantial potential for being widely and deeply explored in forest ecosystem studies.

Associate Professor Lu Hao is the first author, Li Bowen, a 2024 graduate of the School of Information Technology, and Pang Yong, a researcher at the Institute of Resource Information, Chinese Academy of Forestry, are the corresponding authors.

This research was partially funded by the Xiong'an New Area Science and Technology Innovation Special Project of Ministry of Science and Technology of China (2023XAGG0065), the National Natural Science Foundation of China (Grant number 42001376), the National Key Research and Development Program of China (Grant number 2020YFE0200800).

Paper link: https://www.sciencedirect.com/science/article/abs/pii/S0034425724006175

Written by Lu Hao, Xu Zhiying
Translated and edited by Song He
Reviewed by Yu Yangyang