Urban trees provide several vital social and environmental services. Within the field of urban planning, tree information is currently usually obtained through expensive and time-consuming fieldwork. This research presents a multi-algorithm methodology that extracts urban tree information, including tree location, absolute height, crown perimeter, and species (group) from airborne laser scanning (ALS) datasets and high-resolution aerial images. We first determine the location of trees from the ALS dataset. After a filtration step removing the erroneous tree locations, we simulate each location’s canopy based on aerial imagery. Finally, we utilize the extracted canopy images to perform tree species classification with deep learning. The validation assessment showed overall good credibility (>70 %) in urban areas and better performance (90 %) in street areas. Compared to other methods that require additional information collection, our methodology uses common data in city databases, enabling cities to collect and update large-scale tree information in a fast manner and supporting decision-makers with important information on understanding the value of urban green under the context of ecosystem services, urban heat islands, and CO2 mitigations.

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研究报告：基于多算法的城市树木信息提取及其在城市规划中的应用


城市树木提供了多种重要的社会和环境服务。在城市规划领域，树木信息目前通常是通过昂贵且耗时的实地工作获得的。本研究提出了一种多算法方法，该方法从机载激光扫描 （ALS） 数据集和高分辨率航空图像中提取城市树木信息，包括树木位置、绝对高度、树冠周长和物种（组）。我们首先从 ALS 数据集中确定树木的位置。在过滤步骤去除错误的树木位置后，我们根据航拍图像模拟每个位置的树冠。最后，我们利用提取的树冠图像通过深度学习进行树种分类。验证评估显示，城市地区总体可信度良好（>70%），街道地区表现较好（90%）。与其他需要额外信息收集的方法相比，我们的方法使用城市数据库中的通用数据，使城市能够快速收集和更新大规模树木信息，并为决策者提供重要信息，帮助他们了解在生态系统服务、城市热岛和二氧化碳减排背景下城市绿色的价值。