Exploring The Application of Remote Sensing Technology and Classification Methods in Urban Green Space Identification and Classification

Yang Xiang

doi:10.54097/9svfdb68

Authors

Yang Xiang

DOI:

https://doi.org/10.54097/9svfdb68

Keywords:

Remote sensing; urban green space; land use classification; supervised classification; unsupervised classification.

Abstract

Urban green space is a key component of urban ecosystems and plays an irreplaceable role in improving the ecological environment and enhancing the quality of human habitation. High-precision land use classification and urban green space identification are the basis for urban renewal and green infrastructure optimisation. Based on the relevant literature on urban green space and land use classification in recent years, this paper describes the main means and methods of urban green space identification and classification, and analyses and summarizes the characteristics of the methods and their applications. This paper concludes that supervised classification methods tend to have higher accuracy but higher cost, and unsupervised classification methods are prone to deviation in accuracy but lower cost. Finally, this paper analyses the shortcomings of remote sensing image technology and research methods in urban green space identification and classification, such as high accuracy and low error, and looks forward to future research on the spatial and temporal evolution of urban green space.

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