A Self-Supervised Learning Approach for Extracting China Physical Urban Boundaries Based on Multi-Source Data

Abstract

Physical urban boundaries (PUBs) are basic geographic information data for defining the spatial extent of urban landscapes with non-agricultural land and non-agricultural economic activities. Accurately mapping PUBs provides a spatiotemporal database for urban dynamic monitoring, territorial spatial planning, and ecological environment protection. However, traditional extraction methods often have problems, such as subjective parameter settings and inconsistent cartographic scales, making it difficult to identify PUBs objectively and accurately. To address these problems, we proposed a self-supervised learning approach for PUB extraction. First, we used nighttime light and OpenStreetMap road data to map the initial urban boundary for data preparation. Then, we designed a pretext task of self-supervised learning based on an unsupervised mutation detection algorithm to automatically mine supervised information in unlabeled data, which can avoid subjective human interference. Finally, a downstream task was designed as a supervised learning task in Google Earth Engine to classify urban and non-urban areas using impervious surface density and nighttime light data, which can solve the scale inconsistency problem. Based on the proposed method, we produced a 30 m resolution China PUB dataset containing six years (i.e., 1995, 2000, 2005, 2010, 2015, and 2020). Our PUBs show good agreement with existing products and accurately describe the spatial extent of urban areas, effectively distinguishing urban and non-urban areas. Moreover, we found that the gap between the national per capita GDP and the urban per capita GDP is gradually decreasing, but regional coordinated development and intensive development still need to be strengthened.

Study on Spatialization and Spatial Pattern of Population Based on Multi-Source Data—A Case Study of the Urban Agglomeration on the North Slope of Tianshan Mountain in Xinjiang, China

“…Grasslands, as a significant indicator of water source distribution, also reflect population distribution to some extent. Impervious surfaces, typically covered with materials like roofs, parking lots, and roads that have low permeability, are one of the most prominent features of urbanization [55]. The distance to impervious surfaces reflects the boundaries of human-made buildings in the study area, thus influencing the simulation of population distribution.…”

A New Technique for Urban and Rural Settlement Boundary Extraction Based on Spectral–Topographic–Radar Polarization Features and Its Application in Xining, China

“…Mutation detection is essentially a dynamic threshold method. By automatically detecting the mutation points of different land-use types, this method can effectively differentiate urban and rural areas and even urban-rural fringe areas; however, it cannot determine precise boundaries [15][16][17]. The clustering method aggregates similar data and has been widely used in urban boundary extraction, such as global [9,18] and Chinese urban boundaries [19].…”

“…With the continuous increase in open-source remote-sensing data, multi-source datasets were used to extract URSs [22], namely the following: The China physical urban boundary (CPUB) based on Landsat data, nighttime light data, and OpenStreetMap road data [16]; a dataset of urban built-up areas in China (abbreviated as DUBC) based on nighttime light data, the normalized difference vegetation index (NDVI), and land surface temperature (LST) [23]; and global city boundary products based on Landsat images, Sentinel images, and population data (greater than 1500 people per square kilometer) [24]. Although these boundary products have a high degree of consistency in the main urban areas, they are less accurate in suburban and rural areas.…”