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地图综合智能化研究的发展与思考

武芳 杜佳威 钱海忠 翟仁健

武芳, 杜佳威, 钱海忠, 翟仁健. 地图综合智能化研究的发展与思考[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20210687
引用本文: 武芳, 杜佳威, 钱海忠, 翟仁健. 地图综合智能化研究的发展与思考[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20210687
WU Fang, DU Jiawei, QIAN Haizhong, ZHAI Renjian. Overview of the Research Progress and Reflections in Intelligent Map Generalization[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210687
Citation: WU Fang, DU Jiawei, QIAN Haizhong, ZHAI Renjian. Overview of the Research Progress and Reflections in Intelligent Map Generalization[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210687

地图综合智能化研究的发展与思考

doi: 10.13203/j.whugis20210687
基金项目: 

国家自然科学基金(41801396);河南省杰出青年基金(212300410014)

详细信息
    作者简介:

    武芳,博士,教授,主要从事自动地图综合与空间数据智能处理研究。wufang_630@126.com

  • 中图分类号: P283;P237

Overview of the Research Progress and Reflections in Intelligent Map Generalization

Funds: 

The National Natural Science Foundation of China (41801396); the Natural Science Foundation for Distinguished Young Scholars of Henan Province (212300410014).

  • 摘要: 地图综合是地图制图和多尺度空间数据变换的核心与关键技术。20世纪60年代以来,数字地图数据的自动综合研究逐渐展开并取得了长足的进步,囿于人工智能技术的限制,地图综合的智能解决方法虽有不少成果,但距离真正的智能化、实用化仍有一定的距离。近年来,以深度学习为代表的人工智能技术应用于诸多研究领域并取得显著成效,地图综合的智能化研究也有诸多新的尝试。首先,在归纳自动地图综合研究模式的基础上,阐述了智能地图综合研究的必要性;然后,结合人工智能发展历程回顾智能地图综合研究,梳理和分析了基于传统机器学习与基于深度学习的智能地图综合研究现状,并归纳了地图综合智能化研究的主要方法;最后,围绕地图综合智能化研究中的几个热点问题,探讨了智能地图综合的发展趋势。
  • [1] Brassel K E,Weibel R.A Review and Conceptual Framework of Automated Map Generalization[J].International Journal of Geographical Information Systems,1988,2(3):229-244
    [2] Li Z L.Digital Map Generalization at the Age of Enlightenment:A Review of the First Forty Years[J].The Cartographic Journal,2007,44(1):80-93
    [3] Cebrykow P.Cartographic Generalization Yesterday and Today[J].Polish Cartographical Review,2017,49(1):5-15
    [4] Lecordix F,Plazanet C,Lagrange J P.A Platform for Research in Generalization:Application to Caricature[J].GeoInformatica,1997,1(2):161-182
    [5] Li J H,Wu F,Gong X Y,et al.Depth Contour Smoothing Based on the Fitting of Multi-Segment Bezier Curves[J].Marine Geodesy,2018,41(4):382-404
    [6] Du J W,Wu F,Xing R X,et al.An Automated Approach to Coastline Simplification for Maritime Structures with Collapse Operation[J].Marine Geodesy,2021,44(3):157-195
    [7] Hinton G E,Salakhutdinov R R.Reducing the Dimensionality of Data with Neural Networks[J].Science,2006,313(5786):504-507
    [8] Touya G,Zhang X,Lokhat I.Is Deep Learning the New Agent for Map Generalization?[J].International Journal of Cartography,2019,5(2/3):142-157
    [9] Mehryar M,Afshin R,Ameet T.Foundations of Machine Learning[M].Massachusetts:MIT Press,2018
    [10] García-Balboa J L,Ariza-López F J.Generalization-Oriented Road Line Classification by Means of an Artificial Neural Network[J].GeoInformatica,2008,12(3):289-312
    [11] Plazanet C,Bigolin N M,Ruas A.Experiments with Learning Techniques for Spatial Model Enrichment and Line Generalization[J].GeoInformatica,1998,2(4):315-333
    [12] He Y K,Ai T H,Yu W H,et al.A Linear Tessellation Model to Identify Spatial Pattern in Urban Street Networks[J].International Journal of Geographical Information Science,2017,31(8):1541-1561
    [13] He X J,Zhang X C,Xin Q C.Recognition of Building Group Patterns in Topographic Maps Based on Graph Partitioning and Random Forest[J].ISPRS Journal of Photogrammetry and Remote Sensing,2018,136:26-40
    [14] Steiniger S,Lange T,Burghardt D,et al.An Approach for the Classification of Urban Building Structures Based on Discriminant Analysis Techniques[J].Transactions in GIS,2008,12(1):31-59
    [15] Sester M.Knowledge Acquisition for the Automatic Interpretation of Spatial Data[J].International Journal of Geographical Information Science,2000,14(1):1-24
    [16] Karsznia I,Weibel R.Improving Settlement Selection for Small-Scale Maps Using Data Enrichment and Machine Learning[J].Cartography and Geographic Information Science,2018,45(2):111-127
    [17] Lee J,Jang H,Yang J,et al.Machine Learning Classification of Buildings for Map Generalization[J].ISPRS International Journal of Geo-Information,2017,6(10):309
    [18] Jiang B,Harrie L.Selection of Streets from a Network Using Self-Organizing Maps[J].Transactions in GIS,2004,8(3):335-350
    [19] Zhou Q,Li Z L.Use of Artificial Neural Networks for Selective Omission in Updating Road Networks[J].The Cartographic Journal,2014,51(1):38-51
    [20] Zhou Q,Li Z L.A Comparative Study of Various Supervised Learning Approaches to Selective Omission in a Road Network[J].The Cartographic Journal,2017,54(3):254-264
    [21] Jiang B,Nakos B.Line Simplification Using Self-Organizing Maps[C]//ISPRS Workshop on Spatial Analysis and Decision Making,Hong Kong,China,2003
    [22] Cheng B Y,Liu Q,Li X W,et al.Building Simplification Using Backpropagation Neural Networks:A Combination of Cartographers'Expertise and Raster-Based Local Perception[J].GIScience&Remote Sensing,2013,50(5):527-542
    [23] Allouche M K,Moulin B.Amalgamation in Cartographic Generalization Using Kohonen's Feature Nets[J].International Journal of Geographical Information Science,2005,19(8/9):899-914
    [24] Zhang L Q,Deng H,Chen D,et al.A Spatial Cognition-Based Urban Building Clustering Approach and Its Applications[J].International Journal of Geographical Information Science,2013,27(4):721-740
    [25] Sester M.Optimization Approaches for Generalization and Data Abstraction[J].International Journal of Geographical Information Science,2005,19(8/9):871-897
    [26] Yan X F,Chen H,Huang H R,et al.Building Typification in Map Generalization Using Affinity Propagation Clustering[J].ISPRS International Journal of Geo-Information,2021,10(11):732
    [27] Ware J M,Jones C B.Conflict Reduction in Map Generalization Using Iterative Improvement[J].GeoInformatica,1998,2(4):383-407
    [28] Wilson I D,Ware J A.Reducing Graphic Conflict in Scale Reduced Maps Using a Genetic Algorithm[C]//The 7th ICA Workshop on Progress in Automated Map Generalization,Paris,France,2003
    [29] Yang M,Yuan T,Yan X F,et al.A Hybrid Approach to Building Simplification with an Evaluator from a Backpropagation Neural Network[J].International Journal of Geographical Information Science,2022,36(2):280-309
    [30] Park W,Yu K.Hybrid Line Simplification for Cartographic Generalization[J].Pattern Recognition Letters,2011,32(9):1267-1273
    [31] Lamy S,Ruas A,Demazeau Y,et al.The Application of Agents in Automated Map Generalization[C]//The 19th International Cartographic Conference,Ottwa,Canada,1999
    [32] Duchêne C,Cambier C.Cartographic Generalisation Using Cooperative Agents[C]//Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems,Melbourne,Australia,2003
    [33] Touya G,Duchêne C,Ruas A.Collaborative Generalisation:Formalisation of Generalisation Knowledge to Orchestrate Different Cartographic Generalisation Processes[M]//Fabrikant S.I.,Reichenbacher T.,van Kreveld M.,Schlieder C.(eds) Geographic Information Science.Berlin,Heidelberg:Springer,2010:264-278
    [34] Mustiere S.Cartographic Generalization of Roads in a Local and Adaptive Approach:A Knowledge Acquistion Problem[J].International Journal of Geographical Information Science,2005,19(8/9):937-955
    [35] Cetinkaya S,Basaraner M.Characterisation of Building Alignments with New Measures Using C4.5 Decision Tree Algorithm[J].Geodetski Vestnik,2014,58(3):552-567
    [36] García-Balboa J L,Reinoso-Gordo J F,Ariza-López F J.Automated Assessment of Road Generalization Results by Means of an Artificial Neural Network[J].GIScience&Remote Sensing,2012,49(4):558-596
    [37] Harrie L,Stigmar H,Djordjevic M.Analytical Estimation of Map Readability[J].ISPRS International Journal of GeoInformation,2015,4(2):418-446
    [38] Rawat W,Wang Z H.Deep Convolutional Neural Networks for Image Classification:A Comprehensive Review[J].Neural Computation,2017,29(9):2352-2449
    [39] Zhang S,Tong H H,Xu J J,et al.Graph Convolutional Networks:A Comprehensive Review[J].Computational Social Networks,2019,6(1):1-23
    [40] Schmidt R M.Recurrent Neural Networks (RNNs):A Gentle Introduction and Overview[EB/OL].[2019-11-23].https://arxiv.org/abs/1912.05911
    [41] Touya G,Lokhat I.Deep Learning for Enrichment of Vector Spatial Databases:Application to Highway Interchange[J].ACM Transactions on Spatial Algorithms and Systems,2020,6(3):21
    [42] Yan X F,Ai T H,Yang M,et al.Graph Convolutional Autoencoder Model for the Shape Coding and Cognition of Buildings in Maps[J].International Journal of Geographical Information Science,2021,35(3):490-512
    [43] Yan X F,Ai T H,Yang M,et al.A Graph Convolutional Neural Network for Classification of Building Patterns Using Spatial Vector Data[J].ISPRS Journal of Photogrammetry and Remote Sensing,2019,150:259-273
    [44] Yan X F,Ai T H,Yang M,et al.A Graph Deep Learning Approach for Urban Building Grouping[J].Geocarto International,2020:1-24
    [45] Feng Y,Thiemann F,Sester M.Learning Cartographic Building Generalization with Deep Convolutional Neural Networks[J].ISPRS International Journal of Geo-Information,2019,8(6):258
    [46] Courtial A,El Ayedi A,Touya G,et al.Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation[J].ISPRS International Journal of Geo-Information,2020,9(5):338
    [47] Du J W,Wu F,Xing R X,et al.Segmentation and Sampling Method for Complex Polyline Generalization Based on a Generative Adversarial Network[J].Geocarto International,2021:1-23
    [48] Zheng J,Gao Z R,Ma J S,et al.Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection[J].ISPRS International Journal of Geo-Information,2021,10(11):768
    [49] Su B,Li Z L.An Algebraic Basis for Digital Generalization of Area-Patches Based on Morphological Techniques[J].The Cartographic Journal,1995,32(2):148-153
    [50] Shen Y L,Ai T H,Li W D,et al.A Polygon Aggregation Method with Global Feature Preservation Using Superpixel Segmentation[J].Computers,Environment and Urban Systems,2019,75:117-131
    [51] Isola P,Zhu J Y,Zhou T H,et al.Image-to-Image Translation with Conditional Adversarial Networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition,Honolulu,USA,2017
    [52] Kang Y H,Gao S,Roth R E.Transferring Multiscale Map Styles Using Generative Adversarial Networks[J].International Journal of Cartography,2019,5(2/3):115-141
    [53] Ganguli S,Garzon P,Glaser N.GeoGAN:A Conditional GAN with Reconstruction and Style Loss to Generate Standard Layer of Maps from Satellite Images[EB/OL].[2019-4-25].https://arxiv.org/abs/1902.05611
    [54] Shen Y L,Ai T H,Chen H,et al.Multilevel Mapping from Remote Sensing Images:A Case Study of Urban Buildings[J].IEEE Transactions on Geoscience and Remote Sensing,2022,60:1-16
    [55] Christoph M.Interpretable Machine Learning:A Guide for Making Black Box Models Explainable[M].Raleigh:Lulu,2021
    [56] Gahegan M.Fourth Paradigm GIScience?Prospects for Automated Discovery and Explanation from Data[J].International Journal of Geographical Information Science,2020,34(1):1-21
    [57] Guo X,Qian H Z,Wu F,et al.A Method for Constructing Geographical Knowledge Graph from Multisource Data[J].Sustainability,2021,13(19):10602
    [58] Anderson B.Computational Neuroscience and Cognitive Modelling[M].California:SAGE Publications Inc,2014
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出版历程
  • 收稿日期:  2021-12-20
  • 网络出版日期:  2022-03-19

地图综合智能化研究的发展与思考

doi: 10.13203/j.whugis20210687
    基金项目:

    国家自然科学基金(41801396);河南省杰出青年基金(212300410014)

    作者简介:

    武芳,博士,教授,主要从事自动地图综合与空间数据智能处理研究。wufang_630@126.com

  • 中图分类号: P283;P237

摘要: 地图综合是地图制图和多尺度空间数据变换的核心与关键技术。20世纪60年代以来,数字地图数据的自动综合研究逐渐展开并取得了长足的进步,囿于人工智能技术的限制,地图综合的智能解决方法虽有不少成果,但距离真正的智能化、实用化仍有一定的距离。近年来,以深度学习为代表的人工智能技术应用于诸多研究领域并取得显著成效,地图综合的智能化研究也有诸多新的尝试。首先,在归纳自动地图综合研究模式的基础上,阐述了智能地图综合研究的必要性;然后,结合人工智能发展历程回顾智能地图综合研究,梳理和分析了基于传统机器学习与基于深度学习的智能地图综合研究现状,并归纳了地图综合智能化研究的主要方法;最后,围绕地图综合智能化研究中的几个热点问题,探讨了智能地图综合的发展趋势。

English Abstract

武芳, 杜佳威, 钱海忠, 翟仁健. 地图综合智能化研究的发展与思考[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20210687
引用本文: 武芳, 杜佳威, 钱海忠, 翟仁健. 地图综合智能化研究的发展与思考[J]. 武汉大学学报 ● 信息科学版. doi: 10.13203/j.whugis20210687
WU Fang, DU Jiawei, QIAN Haizhong, ZHAI Renjian. Overview of the Research Progress and Reflections in Intelligent Map Generalization[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210687
Citation: WU Fang, DU Jiawei, QIAN Haizhong, ZHAI Renjian. Overview of the Research Progress and Reflections in Intelligent Map Generalization[J]. Geomatics and Information Science of Wuhan University. doi: 10.13203/j.whugis20210687
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