Abstract
Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction.
Highlights
The urban built-up area refers to the nonagricultural construction land developed in the administrative region from the macro perspective; from the micro perspective, it refers to the urban construction land distributed in the urban area with basically perfect municipal public facilities [1]
Taking the urban agglomeration in the central Yunnan Province as an example, this study develops a new urban built-up area extraction method based on the fusion of night light and point of interest (POI) data by wavelet transform, and the urban built-up area is extracted by multi-resolution segmentation, and the accuracy of the result is tested
The POI data: a total number of 449,821 POI data for Kunming, Yunnan Province in March 2019 is obtained in the beginning by using the API (Application Programming Interface) provided by AMap, as this study is about urban built-up areas, only 403,376 data remains after filtering the POI data without actual geographical meaning in Kunming
Summary
The urban built-up area refers to the nonagricultural construction land developed in the administrative region from the macro perspective; from the micro perspective, it refers to the urban construction land distributed in the urban area with basically perfect municipal public facilities [1]. The scope of urban built-up areas is directly related to the level of urbanization [3], that is, the accurate identification of urban build-up area can greatly contribute to the accurate understanding of rapid urbanization. In the cities with a higher urbanization level, the urban build-up area is one of the most important factors that affect the temperature of surface [4,5], the accurate extraction of urban built-up areas can provide a research basis for the study of urban heat island phenomena [6,7]. The expansion of urban built-up areas is accompanied by drastic changes in land use, unbalanced economic development, complex population composition and other prominent urban problems, which require more attention in urban construction and management [9,10]. Accurate extraction of urban built-up areas is crucial to the development of urban boundary, the division of ecological red line and the alleviation of social and environmental problems raised in the process of urbanization [11]
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