Mapping an Urban Boundary Based on Multi-Temporal Sentinel-2 and POI Data: A Case Study of Zhengzhou City

Abstract

Accurately identifying and delineating urban boundaries are the premise for and foundation of the control of disorderly urban sprawl, which is helpful for us to accurately grasp the scale and form of cities, optimize the internal spatial structure and pattern of cities, and guide the expansion of urban spaces in the future. At present, the concept and delineation of urban boundaries do not follow a unified method or standard. However, many scholars have made use of multi-source remote sensing images of various scales and social auxiliary data such as point of interest (POI) data to achieve large-scale, high-resolution, and high-precision land cover mapping and impermeable water surface mapping. The accuracy of small- and medium-scale urban boundary mapping has not been improved to an obvious extent. This study uses multi-temporal Sentinel-2 high-resolution images and POI data that can reflect detailed features of human activities to extract multi-dimensional features and use random forests and mathematical morphology to map the urban boundaries of the city of Zhengzhou. The research results show that: (1) the urban construction land extraction model established with multi-dimensional features has a great improvement in accuracy; (2) when the training sample accounts for 65% of the sample data set, the urban construction land extraction model has the highest accuracy, reaching 96.25%, and the Kappa coefficient is 0.93; (3) the optimized boundary of structural elements with a size of 13 × 13 is selected, which is in good agreement in terms of scope and location with the boundary of FROM-GLC10 (Zhengzhou) and visual interpretations. The results from the urban boundary delineation in this paper can be used as an important database for detailed basic land use mapping within cities. Moreover, the method in this paper has some reference value for other cities in terms of delineating urban boundaries.

Deriving Urban Boundaries of Henan Province, China, Based on Sentinel-2 and Deep Learning Methods

“…The remote sensing data sources used include MODIS [13,14], the Landsat series [15][16][17][18], and DMSP/OLS nighttime light data [19][20][21][22], but the spatial resolution of these remote sensing products is relatively low. High-resolution remote sensing data sources include QuickBird, IKONOS, and Gaofen2 [23][24][25]; these products are very expensive, and Sentinel-2, which is free and has high resolution, has therefore become the data source of choice for many studies [26]. By processing remote-sensing images and extracting remote-sensing information and features, the boundaries of built-up urban areas can be better extracted [10].…”

Using Multiple Sources of Data and “Voting Mechanisms” for Urban Land-Use Mapping

“…Previous studies have shown that, when multitemporal remote-sensing image spectral features are used, the classification effect is much better than with single-temporal remotesensing images [7]. Therefore, in this paper, remote-sensing images for four time periods (months) in Zhengzhou City in 2018 were selected, thus extracting a set of high-dimensional spatial and temporal features.…”

“…Land use has been an important research component for global ecological changes and sustainable social development [2][3][4][5]. As a major carrier of basic human life and daily activities, land merits accurate and timely understanding of urban land-use information, which is extremely important for tapping the inherent development potential of cities, improving urban spatial governance, and ensuring highquality urban development [6][7][8][9].…”

Accurate Identification of Micro-scale Urban Functional Zones from the Perspective of Dialectical Materialism

“…In another study, Zong et al 25 focused on the data sources and combined POI data, remote sensing images and road network datasets to achieve fine-grained classification of urban functional zones. It can be argued that the utilization of POI data in urban functional zone research yields significant advantages, particularly in improving accuracy 26,27 and reducing costs 28,29 . However, a notable shift towards understanding urban functional zones based on POI data in cities has been noticed [30][31][32] .…”