TSC-Net: a roadside tree structure parameter computation network using street-view images

Diameter at Breast Height (DBH) and Tree Height (TH) are key structure parameters for monitoring roadside trees. Traditional field surveys and LiDAR scanning are either inefficient or expensive. Therefore, we propose an innovative method to compute tree structure parameters using low-cost, high-coverage street-view images. Existing image-based methods often rely on fixed scale priors (e.g. fixed camera height) or require manual interpretation, which results in poor generalization, low accuracy, and inefficiency. Inspired by how humans understand the 3D world, we integrate semantic and geometric cues to overcome these challenges. Specifically, we propose the first end-to-end tree structure parameter computation network, named TSC-Net. It makes several contributions: (1) To extract robust semantic and geometry information, we integrate a decoupled dual-branch feature encoder. It strengthens the multimodal information extraction capability through a separated dual-path encoding structure. (2) We design a Multimodal Cue-collaborative Guided Regression Module (MCGRM). The core innovation is that it introduces two auxiliary tasks (i.e. distance regression and tree mask regression), which guide the network to focus on the core semantic and geometric cues related to this tree measurement task. Finally, we develop a new dataset for evaluation, TSC-Net achieves Normalized Root Mean Square Error (NRMSE) of 0.20 for DBH and 0.15 for TH, significantly outperforming existing comparative methods (0.44 and 0.24, respectively). TSC-Net also reduces measurement time from 0.67 h to 0.143 s, offering an efficient solution for roadside tree monitoring.