Research on urban development and security in border areas of China based on deep learning
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摘要: 为探究我国边境城市发展态势,评估城市戍边能力,基于D-LinkNet34深度学习算法对西藏自治区托林镇、狮泉河镇和普兰镇建筑物、道路进行自动化提取,并结合景观指数及人口规模分析边境乡镇发展态势和戍边能力。分析表明: ①基于D-LinkNet深度学习网络的提取方法能够有效地对城市建设用地进一步分类,平均总精度高于80%,IOU值在70%以上。②普兰镇和狮泉河镇斑块分布呈聚集趋势发展,城市扩张趋势减弱,托林镇斑块分布则呈分散趋势发展,城市扩张趋势明显。③建筑面积同常住人口呈线性关系,托林镇2002—2018年建筑面积增加约68.75%,常住人口增加约39.00%; 狮泉河镇2004—2020年建筑面积增加约70.75%,常住人口增加约68.44%; 普兰镇2005—2018年建筑面积增加约68.36%,常住人口增加约25.04%。研究为定量评价边境城市扩张特征及戍边能力提供新方法,同时为建设祖国边疆戍边能力提供参考。Abstract: 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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Key words:
- remote sensing /
- border towns /
- urban development /
- landscape index /
- D-LinkNet
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