Research on urban development and security in border areas of China based on deep learning
MA Xiaoyu1(), ZHANG Xin2,3, LIU Jilei4(), ZHOU Nan2,3, LIU Kejian3, WEI Chunshan5, YANG Peng5
1. School of Earth Sciences and Engineering, Hebei University of Engineering, Handan 056000, China 2. State Key Laboratory of Remote Sensing Science, Institute of Aerospace Information Innovation, Chinese Academy of Sciences, Beijing 100101, China 3. University of Chinese Academy of Sciences, Beijing 100101, China 4. Public Security Remote Sensing Application Engineering Technology Research Center, People’s Public Security University of China, Beijing 100101, China 5. Suzhou Zhexin Information Technology Limited Company, Suzhou 215000, China
In order to explore the development trend of border cities in China and assess the city’s border defense capability, the D-LinkNet34 deep learning algorithm is used to automate the extraction of buildings and roads in Tuolin, Shiquanhe and Pulan towns in Tibet Autonomous Region, and to analyze the development trend and border defense capability of border towns based on landscape index and population size. Analysis results show that: ① The extraction method based on D-LinkNet deep learning network can effectively further classify urban construction land, with average total progress of more than 80% and IOU above 70%.② The distribution of plaques in the towns of Pulan and Shiquanhe shows a trend of aggregation, and the trend of urban expansion weakened. The distribution of plaques in Tuolin Town shows a scattered trend, and the trend of urban expansion is obvious. ③ The building area is linearly related to the resident population, and the building area of Tuolin Town increased by about 68.75%from 2002 to 2018, and the resident population increased by about 39.00%. The building area of Shiquanhe Town increased by about 70.75% from 2004 to 2020, while the resident population increased by about 68.44%. The building area of Pulan Town increased by about 68.36% from 2005 to 2018, while the resident population increased by about 25.04%. This study provides a new method for quantitative evaluation of the expansion characteristics and border defense capability of border cities, as well as a reference for building China’s border defense capability.
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MA Xiaoyu, ZHANG Xin, LIU Jilei, ZHOU Nan, LIU Kejian, WEI Chunshan, YANG Peng. Research on urban development and security in border areas of China based on deep learning. Remote Sensing for Natural Resources, 2022, 34(2): 231-241.
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2022, Vol. 34 
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