创新及科技解决方案

解决方案编号

S-0812

解决方案名称

基于激光雷达的树木评估系统

解决方案描述

This solution uses handheld or drone‑mounted LiDAR to perform 3D scanning of trees, automatically calculating diameter at breast height (DBH), crown diameter, tree height, light transmittance, and inclination angle.

Compared to traditional manual measurements, it improves efficiency by 5 to 10 times, with objective and reproducible data.

The system can produce reports on tree structural parameters, announcing early warnings for hazardous trees (e.g., excessive leaning), and carbon sequestration estimates, facilitating forestry management, urban street tree maintenance, and nature conservation.

应用领域

城市管理

环境

基础设施

康乐及文化

使用的技术

人工智能

电脑视觉

数据分析

深度学习

图像识别

物联网

机器学习

影像分析

使用例子

How it improves public services: Hong Kong has a large number of urban roadside trees, park ornamental trees, and countryside park woodlands. It is required to conduct structural safety inspections on tens of thousands of trees every year to prevent injuries and property damage caused by fallen trees. The traditional approach relies on arborists using visual inspection and manual tools (e.g., hypsometers and tape measures) to measure each tree individually. This method is not only inefficient (taking about 10–15 minutes per tree) but also produces subjective data that is difficult to standardise, and it cannot capture details such as light transmittance inside the canopy or the distribution of branches.

This solution uses handheld or drone‑mounted LiDAR to rapidly scan entire areas, acquiring millions of three‑dimensional point‑cloud data points for each tree in just a few seconds. The backend algorithms automatically compute Diameter at Breast Height ("DBH"), tree height, crown diameter, light transmittance, and inclination angle, and generate a structured tree health report.

Based on the risk classification in the report (e.g., an inclination angle >10° is classified as high risk), management units can prioritise pruning, bracing, or removal, significantly reducing public safety incidents caused by tree falls. At the same time, by comparing scan data from different years, tree growth trends and the impact of pests and diseases can be tracked, enabling science‑based urban forest management.

Benefits:

Labour savings: Tree measurement speed is increased by 5 to 10 times, saving about 60–70% of working hours annually, freeing arborists to focus on risk assessment and preventive measures.

Accident prevention: Early identification of high‑risk trees helps avoid compensation claims and lawsuits resulting from tree falls; a single major incident could cause millions of Hong Kong dollars in losses and reputational damage.

Data value: Accumulated multi‑period point‑cloud data can be used to calculate tree carbon sinks, achieving carbon neutrality goals, and serving as a scientific basis for urban greening policies.

Public satisfaction: Through transparent data reports, citizens can understand the health status of trees in their neighbourhood, enhancing trust in management.

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