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作者

Henrique César de Lima Araújo;Fellipe Silva Martins;Tatiana Tucunduva Philippi Cortese;Giuliano Maselli Locosselli

来源

URBAN FORESTRY & URBAN GREENING,Vol.66,Issue1,Article 127410

语言

英文

关键字

Deep learning;Governance;Green infrastructure;Machine learning;Nature-based solutions;Urban planning;Urban trees

作者单位

Federal University of São Paulo, São Paulo, Brazil;Nove de Julho University (UNINOVE), IT & Knowledge Management Graduate School, São Paulo, Brazil;Nove de Julho University (UNINOVE), Smart and Sustainable Cities Graduate School, São Paulo, Brazil;University of São Paulo, Institute of Advanced Studies, São Paulo, Brazil;Institute of Botany / SIMA / SP, Cluster of Ecology, São Paulo, Brazil;Federal University of São Paulo, São Paulo, Brazil;Nove de Julho University (UNINOVE), IT & Knowledge Management Graduate School, São Paulo, Brazil;Nove de Julho University (UNINOVE), Smart and Sustainable Cities Graduate School, São Paulo, Brazil;University of São Paulo, Institute of Advanced Studies, São Paulo, Brazil;Institute of Botany / SIMA / SP, Cluster of Ecology, São Paulo, Brazil

摘要

Environmental quality and the citizens' well-being largely depend on the urban forests. But managing this natural capital is challenging for its biological complexity and interactions with other environmental, social, and economic aspects of the cities. In line with the current digital revolution with the rise of Smart Cities, the use of Artificial Intelligence (AI) is becoming more common, including in urban forestry. In this systematic review, we evaluated 67 studies on the interplay between AI and urban forestry surveyed on Science Direct and Scopus to provide an overview of the state of the art and identify new research avenues. The sample includes studies in 23 countries and 85 cities, including 5 megacities, comprising the remote assessment of canopy cover and species distribution; ecosystem services assessment; management practices; and socioeconomic aspects of urban forestry. Most studies focused on extant urban forests, with few examples evaluating temporal trends, and only one focused on future scenarios despite the predictive potential of AI. A total of 22 AI methods were employed in these studies. Only half of them point to clear advantages of the chosen methods, such as robustness against missing data, overfitting, collinearity, non-linearity, non-normality, the combination of discrete and continuous variables, and higher accuracy. The choice of these methods depends on the various combinations of aim, timescale, data type, and data source. The application of AI in urban forestry is in full growth and will support decision making to improve livability in the cities.