计算机科学 ›› 2021, Vol. 48 ›› Issue (9): 50-58.doi: 10.11896/jsjkx.210500220

所属专题: 智能数据治理技术与系统

• 智能数据治理技术与系统* 上一篇    下一篇


周新民1,2, 胡宜桂2, 刘文洁2, 孙荣俊2   

  1. 1 湖南工商大学新零售虚拟现实技术湖南省重点实验室 长沙410205
    2 湖南工商大学计算机与信息工程学院 长沙410205
  • 收稿日期:2021-05-30 修回日期:2021-07-27 出版日期:2021-09-15 发布日期:2021-09-10
  • 通讯作者: 周新民(zhouxinmin2699@163.com)
  • 基金资助:

Research on Urban Function Recognition Based on Multi-modal and Multi-level Data Fusion Method

ZHOU Xin-min1,2, HU Yi-gui2, LIU Wen-jie2, SUN Rong-jun2   

  1. 1 Key Laboratory of Hunan Province for New Retail Virtual Reality Technology,Hunan University of Technology and Business,Changsha 410205,China
    2 School of Computer and Information Engineering,Hunan University of Technology and Business,Changsha 410205,China
  • Received:2021-05-30 Revised:2021-07-27 Online:2021-09-15 Published:2021-09-10
  • About author:ZHOU Xin-min,born in 1977,Ph.D,professor,is a member of China Computer Federation.His main research interests include New Smart City and business intelligence and Big Data.
  • Supported by:
    Major Program of the National Natural Science Foundation of China(72091515)

摘要: 城市功能区的划分与识别对分析城市功能区的分布现状和了解城市内部空间结构具有重要意义。这激发了多源地理空间数据融合的需求,特别是城市遥感数据与社会感知数据的融合。然而,如何有效实现城市遥感数据与社会感知数据的融合是一个技术难题。为了实现城市遥感数据与社会感知数据的融合,提高城市功能识别精度,以遥感图像和社会感知数据为例,引入多模态数据融合机制,提出了一种联合深度学习与集成学习的模型来推断城市区域功能。该模型分别利用DenseNet和DPN网络,从多源地理空间数据中提取城市遥感图像特征和社会感知特征,并进行特征级融合、决策级融合以及混合融合的多层级数据融合,对城市功能进行识别。所提模型在URFC数据集上得到了验证,其混合融合总体分类准确度、Kappa系数和平均F1值3个评价指标值分别为74.29%,0.67,71.92%。相比单模态数据的最佳分类方法,所提融合模型的3个评价指标值分别提高了18.83%,0.24,35.46%。实验结果表明,该数据融合模型具有更好的分类性能,能有效融合遥感图像数据和社会感知数据,实现城市区域功能的精准识别。

关键词: 城市功能区识别, 多模态数据融合, 集成学习, 社会感知, 深度学习

Abstract: The division and identification of urban functional areas is of great significance for analyzing the distribution status of urban functional areas and understanding the internal spatial structure of cities.This has stimulated the demand for multi-source geospatial data fusion,especially the fusion of urban remote sensing data and social sensing data.However,how to realize the fusion of urban remote sensing and social sensing data is a technical problem effectively.In order to realize the fusion of urban remote sensing and social sensing data and improve the accuracy of urban function recognition,taking remote sensing images and social sensing data as examples,introducing a multi-modal data fusion mechanism,and proposing a joint deep learning and ensemble learning model to infer urban regional functions.The model uses DenseNet and DPN network to extract urban remote sensing image features and social sensing features from multi-source geospatial data,and carries out multi-level data fusion of feature fusion,decision fusion and hybrid fusion to identify urban functions.The proposed model is verified on the URFC dataset,and these three evaluation index values of hybrid fusion overall classification accuracy,Kappa coefficient and average F1 are 74.29%,0.67,71.92%,respectively.Compared with the best classification method of single modal data,the three evaluation indexes of the proposed fusion model are increased by 18.83%,0.24,35.46% respectively.The experimental results show that the data fusion model has better classification performance,so that it can effectively fuse remote sensing image data and social sensing data,and realize the accurate identification of urban regional functions.

Key words: Deep learning, Ensemble learning, Multi-modal data fusion, Social sensing, Urban function recognition


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