Identification And Localization Of Sustainable Indicators Of Urban "informal Residential Space" Based On Multi-source Remote Sensing Data

In the context of rapid urbanization,the influx of foreign populations into cities for better employment opportunities has also led to an increase in urban villages as cities expand,which in turn has created residential spaces such as slums,squatter settlements,and informal settlements that are in dire need of governance.To guide the development of appropriate policies and programs to ensure adequate housing and slum upgrading for all,the United Nations has raised the need to identify and quantify the proportion of people living in slums,informal settlements,and inadequate housing.Based on SDG11.1.1,this paper proposes Informal Living Space(ILS)as a sustainable development indicator in China's context.Compared with the surrounding areas,ILS has a low living area per capita,dense housing,old and disorderly layout,and poor community environment.Under the new situation,this paper studies the deep learning method to identify and extract ILS based on Very high resolution(VHR)remote sensing images and multi-source remote sensing datasets,and analyzes the distribution characteristics and reasons of ILS in the parts of Beijing,Shanghai and Guangzhou city in 2019,and the results show that the area of ILS identified within the fifth ring in Beijing accounts for the total area within the fifth ring 1.98%,and the distribution showed a trend of marginalization and suburbanization from the center to the south gradually increasing near the fourth and fifth rings,mainly urban villages consisting of brick houses.The area of ILS identified in the outer ring of Shanghai accounts for 0.67% of the total area of the outer ring of Shanghai,and is distributed in the riverine area of Yangpu District,the southwest area of Hongkou District,and part of Huangpu District,with alleyways as the main buildings in the city center and self-built houses in urban villages at the edge of the outer ring.The ILS identified in Guangzhou city accounts for 3.95% of the total area of Guangzhou city,and is distributed in the junction zone between districts,with buildings mainly with compact and high-density characteristics.The ILS in all three cities are characterized by dilapidated houses and small spaces between buildings,which are prone to safety hazards and small street green areas,negatively affecting the well-being of urban residents,and need to be improved by combining the multiple roles of government,market,and society.Comparing with the case of shantytown identification in the Earth Big Data Supporting Sustainable Development Goals report and analyzing the impact of ILS on urban sustainable development,it is concluded that ILS is more applicable to measure the sustainable development of cities.Combined with the identification results it also shows that Shanghai has better sustainable development than Beijing and Guangzhou has the worst sustainable development.Analysis of the relationship between the ILS and the SDGs,as well as most of the causes of the ILS can be found in relation to its SDG indicator.Therefore,the indicator of ILS is a comprehensive indicator that can measure the sustainable development of cities.By making a 3km buffer zone of the epidemic cell sites and calculating the proportion of affected ILS,the results show that the proportions of Beijing,Shanghai and Guangzhou were 70.67%,77.78% and92.31% respectively before the Wuhan epidemic was cleared,and 90.85%,89.47%and 98.81% respectively after the clearing,and the proportions after the clearing were larger than before the clearing,and Shanghai < Beijing < Guangzhou.The resumption of work and production after zeroing led to a large number of foreigners,resulting in a dense population and a high risk of infection with novel coronavirus pneumonia.In view of this,this paper uses the proportion of ILS as a localized indicator to measure the sustainable development of cities.