Identification of densely populated-informal settlements and their role in Chinese urban sustainability assessment

Abstract

China’s National Plan on Implementation of the 2030 Agenda for Sustainable Development identified the upgrading of urban shantytowns, urban villages, and dilapidated houses as an important initiative to implement the Sustainable Development Goal (SDG) 11.1. However, informal housing being used as temporary housing by low-income families (especially in megacities) has resulted in informal settlements that are usually densely populated, dilapidated, and disorganized. Identifying targets based on deep learning and single very high-resolution images remains a challenging task. Here, we used multi-source geographic information data and machine-learning methods to identify and compare the distribution of densely populated‒informal settlements (DPISs) and measured population ratios within the urban areas of three Chinese first-tier cities: Beijing, Shanghai, and Guangzhou. Our results indicate that DPISs occupy 1.98%, 0.67%, and 3.95% of the total area in these study regions, with population densities of 42,800 people/km2, 7,100 people/km2, and 20,800 people/km2, respectively. Further, significant variability existed in the distribution of DPISs, population ratios, and composition of the study areas. DPISs reflected the rapid urbanization process in China and the planning of the city, which can be used as an indicator of sustainable urban development in China.

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