Research Note: Multi-Algorithm-Based urban tree information extraction and Its applications in urban planning

• Multi-algorithmic methodology extracts urban tree data including location, height, perimeter, and species (group). • Automated, fast and large-scale vegetation data acquisition for urban database updates. • The methodology uses widely available city data, while allowing for the use of higher-precision data to optimize accuracy. 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.