TY - JOUR
T1 - Research Note : Multi-Algorithm-Based urban tree information extraction and Its applications in urban planning
AU - Yao, Chaowen
AU - Fabritius, Henna
AU - Fricker, Pia
AU - Dembski, Fabian
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/10/5
Y1 - 2024/10/5
N2 - 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.
AB - 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.
KW - Deep Learning
KW - Multi-algorithm Methodology
KW - Smart Green Cities
KW - Tree Information Database
UR - http://www.scopus.com/inward/record.url?scp=85205480497&partnerID=8YFLogxK
U2 - 10.1016/j.landurbplan.2024.105226
DO - 10.1016/j.landurbplan.2024.105226
M3 - Article
AN - SCOPUS:85205480497
SN - 0169-2046
VL - 253
JO - LANDSCAPE AND URBAN PLANNING
JF - LANDSCAPE AND URBAN PLANNING
M1 - 105226
ER -