A Fast Fusion Model for Multi-Source Heterogeneous Data Of Real Estate Based on Feature Similarity

Li Linhui; Wang Yu; Liu Yueyan; Li Lei; Huang Jincheng; Zhou Yi; Cao Songlin

doi:10.13203/j.whugis20220742

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Li Linhui, Wang Yu, Liu Yueyan, Li Lei, Huang Jincheng, Zhou Yi, Cao Songlin. A Fast Fusion Model for Multi-Source Heterogeneous Data Of Real Estate Based on Feature Similarity[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20220742

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A Fast Fusion Model for Multi-Source Heterogeneous Data Of Real Estate Based on Feature Similarity

1 Wuhan Real Estate Surveying and Mapping Center, Wuhan 430015, China;
2 School of Public Administration, China University of Geosciences, Wuhan 430074, China

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Received Date: August 03, 2023
Available Online: September 13, 2023

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Objectives: Fast fusion of multi-source heterogeneous data of real estate is a difficult problem faced by data integration and database construction in the process of unified real estate registration. The existing multi-source heterogeneous data fusion methods of real estate are unable to accurately construct the relationship describing the spatial entities, and the time cost is too high for the real estate data with huge data scale. Methods: By introducing feature similarity technology, this paper proposes a multi-level fast fusion model to realize batch fusion of real estate multi-source heterogeneous data. Firstly, on the basis of electronization and vectorization of real estate multi-source heterogeneous data, similarity factor is introduced to evaluate real estate multi-source heterogeneous data. Then, a network model based on comprehensive similarity weighting algorithm was designed to calculate the comprehensive similarity of real estate multi-source heterogeneous data in the distribution direction of each similarity factor. Finally, similarity threshold parameters and limited range parameters are used to further improve the accuracy and efficiency of multi-source heterogeneous data fusion. Results: This paper takes real estate data as an example to conduct quantitative analysis of the fast fusion model. The experimental results show that, compared with other methods, the proposed model consumes less time and costs, the average fusion time per thousand houses is 3.29s, and the fusion accuracy reaches 93.5%, which can effectively improve the fusion accuracy and efficiency of multi-source heterogeneous data of real estate.
- Multi-source heterogeneous data,
- Feature similarity,
- Fast fusion,
- Similarity factor,
- Multiple level

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[1]	LI Shifeng. Research and Application of Key Technologies of Multi-source Heterogeneous Data Spatiotemporal Fusion[J]. Geospatial Information,2021,19(10):19-21+25+149. (李仕峰.多源异构数据时空融合关键技术研究与应用[J].地理空间信息,2021,19(10):1921+25+149.)
[2]	Ding Yipeng,Yu Xiali,Zhang Juan,Xu Xuemei. Application of Linear Predictive Coding and Data Fusion Process for Target Tracking by Doppler Through-Wall Radar[J]. IEEE Transactions on Microwave Theory and Techniques,2019,67(3).
[3]	L. Susperregi,A. Arruti,E. Jauregi,B. Sierra,J.M. Martínez-Otzeta,E. Lazkano,A. Ansuategui. Fusing multiple image transformations and a thermal sensor with kinect to improve person detection ability[J]. Engineering Applications of Artificial Intelligence,2013,26(8).
[4]	Shen Huanfeng,Wu Jingan,Cheng Qing,Aihemaiti Mahemujiang,Zhang Chengyue,Li Zhiwei. A Spatiotemporal Fusion Based Cloud Removal Method for Remote Sensing Images With Land Cover Changes[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2019,12(3).
[5]	Li Kin Fun. Smart home technology for telemedicine and emergency management.[J]. Journal of ambient intelligence and humanized computing,2013,4(5).
[6]	Atat Rachad,Liu Lingjia,Wu Jinsong,Li Guangyu,Ye Chunxuan,Yang Yi. Big Data Meet CyberPhysical Systems:A Panoramic Survey[J]. IEEE Access,2018,6(6).
[7]	TU Wei, CAO Jinzhou, GAO Qili, CAO Rui, FANG Zhixiang, YUE Yang, LI Qingquan. Sensing Urban Dynamics by Fusing Multi-sourced Spatiotemporal Big Data[J]. Geomatics and Information Science of Wuhan University, 2020, 45(12):1875-1883. (涂伟, 曹劲舟, 高琦丽, 曹瑞, 方志祥, 乐阳, 李清泉. 融合多源时空大数据感知城市动态[J]. 武汉大学学报(信息科学版), 2020, 45(12):1875-1883.)
[8]	Jie Yu,Shenggao Gu,Wei Zhang. The Intelligent Decision-Making Based on Multi-source Heterogeneous Data Fusion in Manufacturing[J]. International Journal of Computational Intelligence Systems,2020,13(1).
[9]	Mo Huiling,Zheng Haifeng,Gao Min,Feng Xinxin. Multi-Source Heterogeneous Data Fusion Based on Federated Learning[J]. Journal of Computer Research and Development,2022,59(2):478-487. (莫慧凌,郑海峰,高敏,冯心欣.基于联邦学习的多源异构数据融合算法[J].计算机研究与发展,2022,59(2):478-487.)
[10]	XIA Wei,CAI Wenting,LIU Yang,LI Hongjie. Multi-source heterogeneous data fusion of a distribution network based on a joint Kalman filter[J]. Power System Protection and Control,2022,50(10):180-187. (夏伟,蔡文婷,刘阳,李宏杰.基于联合卡尔曼滤波的配电网多源异构数据融合[J].电力系统保护与控制,2022,50(10):180-187.)
[11]	Tang Y L,Wu Y Y,Jiang X Z,et al. Multi-source heterogeneous data fusion technology for natural resource information extraction:A case study of NDVI data in Hanjiang Basin[J]. Geological Survey of China,2021,8(2):74-82. (汤宇磊,吴杨杨,蒋兴征,冯亮,高阳.面向自然资源信息提取的多源异构数据融合技术——以汉江流域NDVI数据为例[J].中国地质调查,2021,8(2):74-82.)
[12]	Li Xiaohan,Wang Jun,Tan Jinghua,Ji Shiyu,Jia Huading. A graph neural network-based stock forecasting method utilizing multi-source heterogeneous data fusion.[J]. Multimedia tools and applications,2022,81(30).
[13]	Wei Zhihao,Jia Kebin,Jia Xiaowei. Multi-scale feature fusion based on multi-source heterogeneous remote sensing data Water body extraction[J]. Remote Sensing Information,2021,36(5):41-48. (魏之皓, 贾克斌, 贾晓未. 多尺度特征融合的多源异构遥感数据水体提取[J]. 遥感信息,2021,36(5):41-48.)
[14]	Xiaomei Yang,Wenqiang Guo,Zhonghua Zhao. A Multi-Source Heterogeneous Data Fusion Method Based on Deep Learning[C]//.Proceedings of 20194th International Industrial Informatics and Computer Engineering Conference(IIICEC 2019).Francis Academic Press,UK,2019:114-117.
[15]	WANG Hongwei, HUANG Chunlin, HOU Jinliang, LI Xiaoying. Estimation of Snow Depth from Multi-source Data Fusion Based on Data Assimilation Algorithm[J]. Geomatics and Information Science of Wuhan University, 2016, 41(6):848-852. (王宏伟, 黄春林, 侯金亮, 李晓英. 基于数据同化算法融合多源数据估算雪深[J]. 武汉大学学报(信息科学版), 2016, 41(6):848-852.)
[16]	Yu Zhang,Yange Wang,Hongwei Ding,Yongzhen Li,Yanping Bai. Deep well construction of big data platform based on multi-source heterogeneous data fusion[J]. International Journal of Internet Manufacturing and Services,2019,6(4).
[17]	DAI Haifa, BIAN Hongwei, WANG Rongying, ZHANG Jiajia. An Improved Multi-sensor Data Adaptive Fusion Method[J]. Geomatics and Information Science of Wuhan University, 2020, 45(10):1602-1609. (戴海发, 卞鸿巍, 王荣颖, 张甲甲. 一种改进的多传感器数据自适应融合方法[J]. 武汉大学学报(信息科学版), 2020, 45(10):1602-1609.)
[18]	ZHANG Bo, ZHANG Meng, WANG Fei, FAN Hongchao. Automatic Data Integration between VGI and Topographic Data[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11):1708-1714. (张博, 张猛, 王非, 范红超. VGI数据与地形图数据的自动融合研究[J]. 武汉大学学报(信息科学版), 2019, 44(11):1708-1714.)
[19]	Jian Liu,X.X. Zhang. Data integration in fuzzy XML documents[J]. Information Sciences,2014,280(280).
[20]	Khalili Alireza. An XML-based approach for geo-semantic data exchange from BIM to VR applications[J]. Automation in Construction,2021,121(121).
[21]	LYU Fengfeng. Research on Automatic Analysis Method of Land Survey Data Based on FME[J]. Geomatics & Spatial Information Technology,2021,44(6):105-106. (吕锋锋.基于FME国土调查数据自动化分析方法研究[J].测绘与空间地理信息,2021,44(6):105-106.)
[22]	HE Siyu,ZHAO Kongyang.Research on Downsizing Technology of Land Use Status Map Based on FME[J]. Geomatics & Spatial Information Technology,2021,44(6):181-185+190. (何思宇, 赵孔阳. 基于FME的土地利用现状图缩编技术研究[J]. 测绘与空间地理信息,2021,44(6):181-185+190.)
[23]	Li Chenchao. Analysis of the real estate registration data integration key points[J]. Housing and Real Estate,2017(21):20-21. (李晨超.不动产登记数据整合要点探析[J].住宅与房地产,2017(21):20-21.)
[24]	McElhaney R.M.. Algorithms for graphics and image processing[J]. Proceedings of the IEEE,1983,71(9).

[1]	LIU Shuo, ZHANG Lei, LI Jian. A Modified Wide Lane Bootstrapping Ambiguity Resolution Algorithm[J]. Geomatics and Information Science of Wuhan University, 2018, 43(4): 637-642. DOI: 10.13203/j.whugis20150462
[2]	Wang Bing, Sui Lifen, Wang Wei, Ma Cheng. Rapid Resolution of Integer Ambiguity in Integrated GPS/Gyro Attitude Determination[J]. Geomatics and Information Science of Wuhan University, 2015, 40(1): 128-133.
[3]	FENG Wei, HUANG Dingfa, YAN Li, LI Meng. GNSS Dual-Frequency Integer Relationship Constrained Ambiguity Resolution[J]. Geomatics and Information Science of Wuhan University, 2012, 37(8): 945-948.
[4]	QIU Lei, HUA Xianghong, CAI Hua, WU Yue. Direct Calculation of Ambiguity Resolution in GPS Short Baseline[J]. Geomatics and Information Science of Wuhan University, 2009, 34(1): 97-99.
[5]	WANG Xinzhou, HUA Xianghong, QIU Lei. A New Method for Integer Ambiguity Resolution in GPS Deformation Monitoring[J]. Geomatics and Information Science of Wuhan University, 2007, 32(1): 24-26.
[6]	LIU Zhimin, LIU Jingnan, JIANG Weiping, LI Tao. Ambiguity Resolution of GPS Short-Baseline Using Genetic Algorithm[J]. Geomatics and Information Science of Wuhan University, 2006, 31(7): 607-609.
[7]	LOU Yidong, LI Zhenghang, ZHANG Xiaohong. A Method of Short Baseline Solution without Cycle Slip Detection and Ambiguity Resolution[J]. Geomatics and Information Science of Wuhan University, 2005, 30(11): 995-998.
[8]	YANG Rengui, OU Jikun, WANG Zhenjie ZHAO Chunmei, . Searching Integer Ambiguities in Single Frequency Single Epoch by Genetic Algorithm[J]. Geomatics and Information Science of Wuhan University, 2005, 30(3): 251-254.
[9]	P. J. G. Teunissen. A New Class of GNSS Ambiguity Estimators[J]. Geomatics and Information Science of Wuhan University, 2004, 29(9): 757-762.
[10]	Chen Yongqi. An Approach to Validate the Resolved Ambiguities in GPS Rapid Positioning[J]. Geomatics and Information Science of Wuhan University, 1997, 22(4): 342-345.

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A Fast Fusion Model for Multi-Source Heterogeneous Data Of Real Estate Based on Feature Similarity

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