Objective The structural stability of tall trees is central to the sustainable management of landscape forests, and their precise monitoring and information extraction have become key tasks in urban green space management. However, understory clutter, such as supports and shrubs, often obscures or confuses trunk point clouds, limiting the accuracy of forest information acquisition. To address this issue, this study proposes an automatic understory clutter filtering method to obtain clear and accurate structural information of tall tree trunks, thereby supporting digital monitoring and refined management of urban forests.

Method This paper presents UFNet, an intelligent filtering method based on MLS point clouds. To accurately extract local geometric features within voxels, the method first enhances the original point cloud features through a voxel feature encoding module. Subsequently, a submanifold sparse convolution module is employed to efficiently extract deep semantic information while preserving data sparsity, thus avoiding ineffective feature diffusion in complex forest scenes. Finally, a hierarchical encoder-decoder architecture is used to achieve multi-scale feature fusion, enhancing the perception of small clutter objects and tree trunk boundaries, thereby enabling precise automatic filtering of understory clutter.

Result UFNet demonstrates robust semantic segmentation performance in complex understory environments, effectively distinguishing clutter from trunks. In experiments conducted in forest plots at the Shanghai Botanical Garden, UFNet achieved an overall precision of 88.72%, recall of 88.65%, F₁-score of 88.69%, and Intersection over Union (IoU) of 79.67%. Compared with five other deep learning methods, UFNet improved these four metrics by 3.48–10.21, 5.04–11.37, 4.27–10.80, and 6.63–15.88 percentage points, respectively. Following clutter filtering, the point cloud structure became markedly clearer, with the F₁-score for individual tree segmentation increasing to 94.64% and the IoU rising to 89.83%.

Conclusion UFNet exhibits outstanding capability in understory clutter filtering and can effectively enhance the accuracy of structural information acquisition for tall trees. It provides critical technical support for 3D modeling and refined management of tree-dominated urban landscape forests.