TSC-Net: A roadside tree structure parameter computation network using street-view images
Abstract: Accurate carbon storage estimation of roadside trees depends on structure parameters such as Diameter at Breast Height (DBH) and Tree Height (TH). Traditional field surveys and LiDAR scanning are either inefficient or expensive. So, 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, TSCNet achieved 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 0.67h to 0.026s, offering an efficient solution for roadside tree monitoring, carbon storage estimation,
- 2025-07-24: Code are available! 🎉
The code has been trained on:
- Ubuntu 20.04
- CUDA 11.3
- Python 3.9.18
- Pytorch 1.12.1
- GeForce RTX 4090
$\times$ 1.
- First, create the conda environment:
conda create -n tscnet python=3.9 conda activate tscnet pip install -r requirements.txt - Second, Install the GroundingDINO
git clone https://github.com/IDEA-Research/GroundingDINO.git cd GroundingDINO/ pip install -e .
You can download the test data from GoogleDrive, and put it in folder data/.
You can download the pretrained model from GoogleDrive, and put it in folder checkpoints/.
Our model only needs one image as input, you can simply run it through
python infer.py - img_path "your_image"
We sincerely thank the excellent projects:
- GroundingDINO for excellent object detection performance.