基于多源信息与深度学习特征提取的人口空间抽样方法

doi:10.13474/j.cnki.11-2246.2021.0238

测绘通报 ›› 2021, Vol. 0 ›› Issue (8): 42-47.doi: 10.13474/j.cnki.11-2246.2021.0238

基于多源信息与深度学习特征提取的人口空间抽样方法

淳锦¹, 张新长², 郭海京¹, 张建国³, 金诗程¹

1. 广东省国土资源测绘院, 广东广州 510500;
2. 广州大学地理科学与遥感学院, 广东广州 510006;
3. 湖南博通信息股份有限公司, 湖南长沙 410007

收稿日期:2020-12-10 修回日期:2021-07-01 出版日期:2021-08-25 发布日期:2021-08-30
通讯作者: 张新长。E-mail:eeszxc@mail.sysu.edu.cn
作者简介:淳锦(1994-),女,硕士,助理工程师,主要从事GIS理论及应用、测绘遥感方法等研究。E-mail:chunj@mail2.sysu.edu.cn
基金资助:
国家自然科学基金面上项目（42071441）；广东省省级科技计划（2018B020207002）；国土空间规划多源数据融合与更新合作研发项目（521023）

Spatial sampling of population based on multi-source information and deep learning feature extraction

CHUN Jin¹, ZHANG Xinchang², GUO Haijing¹, ZHANG Jianguo³, JIN Shicheng¹

1. Surveying and Mapping Institute Lands and Resource Department of Guangdong Province, Guangzhou 510500, China;
2. School of Geographical Sciences and Remote Sensing, Guangzhou University, Guangzhou 510006, China;
3. Hunan Botong Information Co., Ltd., Changsha 410007, China

Received:2020-12-10 Revised:2021-07-01 Online:2021-08-25 Published:2021-08-30

摘要/Abstract

摘要： 人口抽样调查是通过人口样本估算区域人口总体的一种手段。由于人口分布通常具有空间差异性，传统的抽样调查理论难以满足日益增长的空间抽样需求，合理高效的人口空间抽样调查方法对于人口统计、研究人类活动、解决城市问题等有重要意义。本文提出一种基于多源信息与深度学习特征提取的人口空间抽样方法。在不透水面信息的辅助下，利用四叉树分割进行分层抽样，初步选择出可能存在人口分布的调查样本，并通过深度学习的常用模型——卷积神经网络估算样本建筑物密度，以辅助最终调查样本的选择与调查方案的制定。研究结果证明，该方法能够有效地筛选与人口分布密切相关的抽样区域，排除大量的无用样本，提高了人口调查的效率，节约了大量调查成本。

关键词: 多源信息, 人口抽样, 四叉树, 深度学习, 特征提取

Abstract: Population sampling survey is a means to estimate the population of a region through population samples. Due to the spatial difference of population distribution, the traditional sampling survey theory is difficult to meet the growing demand of spatial sampling. The research on reasonable and efficient population spatial sampling survey method is of great significance to population statistics, human activities and urban problems. This paper proposes a population spatial sampling method based on multi-source information and deep learning feature extraction. Firstly, we use quadtree segmentation for stratified sampling with the help of impervious surface information, initially select the survey samples that may have population distribution.Secondly, we estimate building density of sample by convolution neural network that is a common model of deep learning to assist in the final sample selection and survey scheme formulation. The results show that this method can effectively screen the sampling areas closely related to population distribution, eliminate a large number of useless samples, improve the efficiency of population survey and save a lot of survey costs.

Key words: multi-source information, population sampling, quadtree, deep learning, feature extraction

中图分类号:

P208

淳锦, 张新长, 郭海京, 张建国, 金诗程. 基于多源信息与深度学习特征提取的人口空间抽样方法[J]. 测绘通报, 2021, 0(8): 42-47.

CHUN Jin, ZHANG Xinchang, GUO Haijing, ZHANG Jianguo, JIN Shicheng. Spatial sampling of population based on multi-source information and deep learning feature extraction[J]. Bulletin of Surveying and Mapping, 2021, 0(8): 42-47.

参考文献

[1] 胡英.关于人口统计调查方法体系存在的问题和改革设想[J]. 统计研究, 2018, 35(4):94-103.
[2] SMITH S K, CODY S. An evaluation of population estimates in florida:April 1, 2000[J]. Population Research & Policy Review, 2004, 23:1-24.
[3] 李素, 庄大方.基于RS和GIS的人口估计方法研究综述[J]. 地理科学进展, 2006(1):109-121.
[4] 姜成晟, 王劲峰, 曹志冬.地理空间抽样理论研究综述[J]. 地理学报, 2009, 64(3):368-380.
[5] CHEW R F, AMER S, JONES K, et al. Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery[J]. International Journal of Health Geographics, 2018, 17(1):12.
[6] CAJKA J, AMER S, RIDENHOUR J, et al. Geo-sampling in developing nations[J]. International Journal of Social Research Methodology, 2018, 21(6):729-746.
[7] ZHU H L, LI Y, LIU Z L, et al. Estimating the population distribution in a county area in China based on impervious surfaces[J]. Photogrammetric Engineering & Remote Sensing, 2015, 81(2):155-163.
[8] AZAR D, GRAESSER J, ENGSTROM R, et al. Spatial refinement of census population distribution using remotely sensed estimates of impervious surfaces in Haiti[J]. International Journal of Remote Sensing, 2010, 31(21):5635-5655.
[9] GALWAY L P, BELL N, SHATARI S, et al. A two-stage cluster sampling method using gridded population data, a GIS, and Google Earth imagery in a population-based mortality survey in Iraq[J]. International Journal of Health Geographics, 2012, 11:12.
[10] ELSEY H, THOMSON D R, LIN R Y, et al. Addressing inequities in urban health:do decision-makers have the data they need? report from the urban health data special session at international conference on urban health Dhaka 2015[J]. Journal of Urban Health, 2016, 93(3):526-537.
[11] MAGGIORI E, TARABALKA Y, CHARPIAT G, et al. Can semantic labeling methods generalize to any city? the inria aerial image labeling benchmark[C]//Proceedings of International Geoscience and Remote Sensing Symposium (IGARSS).[S.l.]:IEEE, 2017:3226-3229.
[12] 刘文涛, 李世华, 覃驭楚.基于全卷积神经网络的建筑物屋顶自动提取[J]. 地球信息科学学报, 2018, 20(11):1562-1570.
[13] 乔文凡, 慎利, 戴延帅, 等. 联合膨胀卷积残差网络和金字塔池化表达的高分影像建筑物自动识别[J]. 地理与地理信息科学, 2018, 34(5):56-62.
[14] 李道纪, 郭海涛, 张保明, 等. 结合双视觉全卷积网络的遥感影像地物提取[J]. 中国图象图形学报, 2020, 25(3):535-545.
[15] 郭利进, 师五喜, 李颖, 等. 基于四叉树的自适应栅格地图创建算法[J]. 控制与决策, 2011, 26(11):1690-1694.
[16] LI L J, SU H, LI F F, et al. Object bank:a high-level image representation for scene classification & semantic feature sparsification[M]//TODDK L. Advances in Neural Information Processing Systems.[S.l.]:The MIT Press, 2006.
[17] SUN Y, CHEN Y H, WANG X G, et al. Deep learning face representation by joint identification-verification[C]//Proceedings of International Conference on Neural Information Processing Systems.[S.l.]:arXiv e-prints, 2014.
[18] LIU G P, LIU H, LI M N, et al. Stable classification with limited sample:transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017[J]. Science Bulletin, 2019, 64(6):370-373.
[19] JIA Y Q, SHELHAMER E, DONAHUE J, et al. Caffe:convolutional architecture for fast feature embedding[C]//Proceedings of the 22nd AcM International Conference on Multimedia. New York:Association for Computing Machinery, 2014.

基于多源信息与深度学习特征提取的人口空间抽样方法

Spatial sampling of population based on multi-source information and deep learning feature extraction

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	岳胜杰, 王红旗, 刘群坡, 赵荣亮. 结合扩展卡尔曼滤波与基于点线的最近点迭代扫描匹配算法的机器人位姿自适应估计[J]. 测绘通报, 2022, 0(7): 49-53.
[2]	刘立, 董先敏, 刘娟, 文学虎. 人机融合智能的遥感解译生产新方法[J]. 测绘通报, 2022, 0(7): 118-123,137.
[3]	杨俊, 刘灵辉. 加权全极化SAR图像分类下的功能区土地时空变化特征提取[J]. 测绘通报, 2022, 0(7): 138-142,153.
[4]	蔡柔丹. 一种基于用户异步轨迹的身份识别智能方法[J]. 测绘通报, 2022, 0(7): 158-162,167.
[5]	滕文鑫, 张建辰, 邵杰. 集成时空邻近与卷积网络车道级高精度定位算法[J]. 测绘通报, 2022, 0(6): 1-5,61.
[6]	张晋赫, 秦育罗, 张在岩, 宋伟东, 朱洪波. 复杂场景下农村道路裂缝分割方法[J]. 测绘通报, 2022, 0(5): 74-78,88.
[7]	师芸, 石龙龙, 牛敏杰, 赵侃. 基于单阶段实例分割网络的黄土滑坡多任务自动识别[J]. 测绘通报, 2022, 0(4): 26-31.
[8]	王雪, 梁珂, 隋立春, 钟棉卿, 朱剑锋. 膨胀卷积与金字塔表达的深度学习模型用于农村建筑物提取[J]. 测绘通报, 2022, 0(4): 61-65.
[9]	高鸣, 周鑫鑫, 刘琦, 杨光迪, 吴长彬. 深度学习网络支持下的农房侵占耕地自动化监测[J]. 测绘通报, 2022, 0(3): 47-53.
[10]	孙琴琴, 蔡国印, 杨柳忠, 张宁, 杜明义. 城市尺度典型地表要素综合提取方法研究[J]. 测绘通报, 2022, 0(2): 62-66.
[11]	张自强, 刘涛, 杜萍, 锁旭宏, 杨国林. 图残差神经网络支持下的建筑物群组模式分类[J]. 测绘通报, 2022, 0(1): 1-7.
[12]	车子杰, 高飞, 吴兆福, 李振轩. 基于改进U-Net网络的多源遥感影像洪涝灾害信息提取与变化分析[J]. 测绘通报, 2022, 0(1): 26-32.
[13]	程瑞, 高建, 邢强, 孙中昶. 一种优化的Faster R-CNN小目标检测方法[J]. 测绘通报, 2021, 0(9): 21-27.
[14]	丁鸽, 燕立爽, 彭健, 朱传广, 黄雪亭. 基于RANSAC算法的隧道点云横断面提取[J]. 测绘通报, 2021, 0(9): 120-123.
[15]	耿万轩, 周维勋, 金双根. 基于LDCNN特征提取的多核SVM高分辨率遥感影像场景分类[J]. 测绘通报, 2021, 0(8): 14-21.