气候变化领域集成服务门户(CCPortal): Progressing towards Estimates of Local Emissions from Trees in Cities: A Transdisciplinary Framework Integrating Available Municipal Data, AI, and Citizen Science

CCPortal

DOI	10.3390/app14010396
	Progressing towards Estimates of Local Emissions from Trees in Cities: A Transdisciplinary Framework Integrating Available Municipal Data, AI, and Citizen Science
	Mayer, Julia; Memmel, Martin; Ruf, Johannes; Patel, Dhruv; Hoff, Lena; Henninger, Sascha
发表日期	2024
EISSN	2076-3417
起始页码	14
结束页码	1
卷号	14 期号:1
英文摘要	Urban tree cadastres, crucial for climate adaptation and urban planning, face challenges in maintaining accuracy and completeness. A transdisciplinary approach in Kaiserslautern, Germany, complements existing incomplete tree data with additional precise GPS locations of urban trees. Deep learning models using aerial imagery identify trees, while other applications employ street view imagery and LIDAR data to collect additional attributes, such as height and crown width. A web application encourages citizen participation in adding features like species and improving datasets for further model training. The initiative aims to minimize resource-intensive maintenance conducted by local administrations, integrate additional features, and improve data quality. Its primary goal is to create transferable AI models utilizing aerial imagery and LIDAR data that can be applied in regions with similar tree populations. The approach includes tree clusters and private trees, which are essential for assessing allergy and ozone potential but are usually not recorded in municipal tree cadastres. The paper highlights the potential of improving tree cadastres for effective urban planning in a transdisciplinary approach, taking into account climate change, health, and public engagement.
英文关键词	urban tree cadastre; deep learning; data quality; AI; open source; urban planning; urban data; citizen science; local emissions; microclimate
语种	英语
WOS研究方向	Chemistry ; Engineering ; Materials Science ; Physics
WOS类目	Chemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS记录号	WOS:001139259300001
来源期刊	APPLIED SCIENCES-BASEL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/294142
作者单位	German Research Center for Artificial Intelligence (DFKI)
推荐引用方式 GB/T 7714	Mayer, Julia,Memmel, Martin,Ruf, Johannes,et al. Progressing towards Estimates of Local Emissions from Trees in Cities: A Transdisciplinary Framework Integrating Available Municipal Data, AI, and Citizen Science[J],2024,14(1).
APA	Mayer, Julia,Memmel, Martin,Ruf, Johannes,Patel, Dhruv,Hoff, Lena,&Henninger, Sascha.(2024).Progressing towards Estimates of Local Emissions from Trees in Cities: A Transdisciplinary Framework Integrating Available Municipal Data, AI, and Citizen Science.APPLIED SCIENCES-BASEL,14(1).
MLA	Mayer, Julia,et al."Progressing towards Estimates of Local Emissions from Trees in Cities: A Transdisciplinary Framework Integrating Available Municipal Data, AI, and Citizen Science".APPLIED SCIENCES-BASEL 14.1(2024).