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Accurate mapping of built-up land is essential for urbanization monitoring and ecosystem research. At present, remote sensing is one of the primary means used for real-time and accurate surveying and mapping of built-up land, due to the long time series and multi-information advantages of existing remote sensing images and the ability to obtain highly precise year-by-year built-up land maps. In this study, we obtained feature-enhanced data regarding built-up land from Landsat images and phenology-based algorithms and proposed a method that combines the use of the Google Earth Engine (GEE) and deep learning approaches. The Res-UNet++ structural model was improved for built-up land mapping in Guangdong from 1991 to 2020. Experiments show that overall accuracy of built-up land map in the study area in 2020 was 0.99, the kappa coefficient was 0.96, user accuracy of built-up land was 0.98, and producer accuracy was 0.901. The trained model can be applied to other years with good results. The overall accuracy (OA) of the assessment results every five years was above 0.97, and the kappa coefficient was above 0.90. From 1991 to 2020, built-up land in Guangdong has expanded significantly, the area of built-up land has increased by 71%, and the proportion of built-up land has increased by 3.91%. Our findings indicate that the combined approach of GEE and deep learning algorithms can be developed into a large-scale, long time-series of remote sensing classification techniques framework that can be useful for future land-use mapping research. [ABSTRACT FROM AUTHOR]

Inter-regional trade contributes to the expansion of built-up land and carbon storage loss, which results in inequality in unequal exchange. Revealing this inequality can help socio-environmental sustainability be better achieved. This paper uses a multi-regional input-output model to analyze the virtual built-up land flows embodied in the inter-provincial trade in China, and calculates the carbon storage loss caused by the expansion of built-up land. The inter-provincial trade inequality in China is discussed subsequently. The results show that regional unfair trade exists in China, and less developed regions are in an inferior position. Compared to less developed regions, developed regions obtain more benefits from inter-provincial trade, in both economic and environmental aspects. Regionally, the expansion of built-up land mainly occurs in the eastern and southern regions, which lost much carbon storage, especially Guangdong and Yunnan. Coastal regions had a high built-up land utilization efficiency, followed by the central regions. The built-up land interactions are mainly concentrated in the central and eastern regions, and the degree of trade participation in less developed regions is low. [ABSTRACT FROM AUTHOR]