Mapping Urban Areas Using a Combination of Remote Sensing and Geolocation Data

Abstract

Urban areas are essential to daily human life; however, the urbanization process also brings about problems, especially in China. Urban mapping at large scales relies heavily on remote sensing (RS) data, which cannot capture socioeconomic features well. Geolocation datasets contain patterns of human movement, which are closely related to the extent of urbanization. However, the integration of RS and geolocation data for urban mapping is performed mostly at the city level or finer scales due to the limitations of geolocation datasets. Tencent provides a large-scale location request density (LRD) dataset with a finer temporal resolution, and makes large-scale urban mapping possible. The objective of this study is to combine multi-source features from RS and geolocation datasets to extract information on urban areas at large scales, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility, and road networks. The random forest (RF) classifier is introduced to deal with these high-dimension features on a 0.01 degree grid. High spatial resolution land cover (LC) products and the normalized difference built-up index from Landsat are used to label all of the samples. The RF prediction results are evaluated using validation samples and compared with LC products for four typical cities. The results show that night-time lights and LRD features contributed the most to the urban prediction results. A total of 176,266 km2 of urban areas in China were extracted using the RF classifier, with an overall accuracy of 90.79% and a kappa coefficient of 0.790. Compared with existing LC products, our results are more consistent with the manually interpreted urban boundaries in the four selected cities. Our results reveal the potential of Tencent LRD data for the extraction of large-scale urban areas, and the reliability of the RF classifier based on a combination of RS and geolocation data.

Optical Remote Sensing

“…Recent urban applications of RS comprise urban green spaces mapping, aerosol monitoring, urban heat island effect, automatic feature extraction (e.g., roads, buildings, and trees), relationships between land-use and surface temperature, 3-dimensional geometric models for urban heat island, urban energy-efficiency models, and mapping migrant housing in mega-urban centers (Blaschke et al 2011;Hamdi 2010;Jin et al 2011;Hofmann et al 2011;Miyazaki et al 2011;Hermosilla et al 2011;Rinner and Hussain 2011;Hay et al 2011;Geiß et al 2011;Liu and Zhang 2011;d'Oleire-Oltmanns et al 2011). Also, some modern urban RS methods are focusing on integrating multiple RS (night light imagery and multispectral indices) and geolocation datasets using machine learning approaches for urban informatics application of RS (Xia et al 2019).…”

COVID-19 Risk Assessment: Contributing to Maintaining Urban Public Health Security and Achieving Sustainable Urban Development

“…With this character, POI can present the spatial differences among various urban factors preferably, with which POI is becoming more and more prevalent in relevant research about geographical space [22]. What's more, POI data has indeed contributed to the simulating of urban spatial structure [23], as well as the analysis of urban hot spots [24,25]. As for the Tencent-Yichuxing data, it is a macro spatial population mobility index of certain time quantum generating by accessing users' location information of the APP belongs to Tencent.…”

An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data

“…For instance, Hu et al [33] constructed POI density, NDVI, NVBI and other characteristics from Sina Weibo POIs and Landsat remote sensing images to measure and explore the feature similarity between different land use types. Xia et al [34] developed an approach to combine multisource features from remote sensing and geolocation datasets, including night-time lights, vegetation cover, land surface temperature, population density, LRD, accessibility and road networks, to extract urban areas at large scales. Zhang et al [35] proposed a Hierarchical Semantic Cognition framework for the classification of urban functional zones based on objects segmented from the remote sensing images and labeled with nearby POI information.…”