典型文献
Investigation on the Relationship between Population Density and Satellite Image Features—a Deep Learning Based Approach
文献摘要：
Timely and accurate population statistic data plays an important role in many fields.To illustrate the demographic characteristics,population density is a crucial factor in evaluating population data.With a dynamic regional migration in population,it is a challenging job to evaluate population density without a census-based survey.We present the approach to classify satellite images in different magnitudes in population density and execute the comparative experiment to discuss the factors that influence the identification to the images with the deep learning approach.In this paper,we use satellite imagery and community population density data.With convolutional neural networks,we evaluated the performance of CNN on population estimation with satellite images,found the features that are important in population estimation,and then perform the sensitive analysis.
文献关键词：
中图分类号：
作者姓名：
Junxiang ZHANG;Peiran LI;Haoran ZHANG;Xuan SONG
作者机构：
Southern University of Science and Technology-University of Tokyo Joint Research Center for Super Smart Cities,Department of Computer and Engineering,Southern University of Science and Technology,Shenzhen 518000,China;Center for Spatial Infor-mation Science,The University of Tokyo,Kashiwa-shi 277-8568,Japan;School of Urban Planning and Design,Peking Uni-versity,Shenzhen 518000,China
文献出处：
引用格式：
[1]Junxiang ZHANG;Peiran LI;Haoran ZHANG;Xuan SONG-.Investigation on the Relationship between Population Density and Satellite Image Features—a Deep Learning Based Approach)[J].测绘学报（英文版）,2022(04):50-58
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B类：
Investigation,Relationship,between,Population,Density,Satellite,Image,Features,Deep,Learning,Based,Approach,Timely,accurate,population,statistic,data,plays,important,role,many,fields,To,illustrate,demographic,characteristics,density,crucial,evaluating,With,dynamic,regional,migration,challenging,job,without,census,survey,We,present,approach,classify,satellite,images,different,magnitudes,execute,comparative,experiment,discuss,factors,that,influence,identification,deep,learning,this,paper,use,imagery,community,convolutional,neural,networks,evaluated,performance,estimation,found,features,are,then,sensitive,analysis
AB值：
0.620374
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