浏览全部资源
扫码关注微信
纸质出版日期： 2021-10-07 ，
扫 描 看 全 文
刘浩，薛梅.2021.虚拟地理环境下的地理空间认知初步探索.遥感学报，25（10）： 2027-2039
Liu H and Xue M. 2021. Exploring the use of virtual geographic environments for geo-spatial cognition research. National Remote Sensing Bulletin， 25（10）：2027-2039
地理空间认知是人类获取地理空间知识、认知地理环境的重要方法和手段，虚拟地理环境VGEs（Virtual Geographic Environments）作为继地图和地理信息系统（GIS）之后的新一代地理分析工具，在地理空间认知方面具有显著优势，但目前相关研究尚处于概念探讨和框架搭建阶段，缺乏对地理空间认知内涵及相关技术方法的论述和研究。本文以经典的地理学6问题求解为导向，从地理本体认知、地理过程认知和地理行为认知3个层次，阐述了VGEs地理空间认知的内涵和基本内容；进一步重点探讨了实现VGEs地理空间认知的相关技术方法，包括城市空间表达与城市计算、多模式人机交互、地理知识图谱与空间推理、地理过程模拟、地理行为模式识别与情感计算等方面。以重庆为例开展了应用案例实践，展示了如何利用相关技术方法实现对地理环境和地理空间对象的认知和理解。本文提出的总体框架和技术体系为VGEs地理空间认知的深入开展和技术实现提供了新的思路和解决方案，有助于将该项工作从理论探讨阶段推向技术实现阶段。
Geo-spatial cognition is an important method for humans to acquire geospatial knowledge and recognize geographical environment. For a long time
people carried out geo-spatial cognition based on maps and GIS
but it has been proven difficult due to the disadvantages of maps and GIS on geospatial expression
understanding of geographical process
and human-computer interaction. With the development of Virtual Geographic Environments (VGEs)
people realize that VGEs have become important new tools for geo-spatial cognition because they are in accordance with the cognition habits in actual living. As an important new direction of geographic information science
VGE-based geo-spatial cognition has been extensively studied in recent years. However
in general
existing studies are still at the primary stage; they mostly focus on the concept
cognitive characteristics
and the preliminary framework. However
studies on the connotation and relevant technical methods are few.
In accordance with the solution of the six classical geographical questions
the basic contents of VGE-based geo-spatial cognition from the three levels of geographical ontology cognition
geographical process cognition
and geographical behavior cognition are elaborated. Among them
the geographical ontology cognition solves the questions of “what
where
when
and their relationship.” Geographical process cognition solves the questions of “why is it there
how does it form
and how will it develops.” Geographic behavioral cognition solves the questions of “what’s the effect
what role does it play
and how it can be used.” Then
the relevant technical methods are discussed to realize VGE-based geo-spatial cognition
including urban spatial representation and urban computing
multimode human–computer interaction
geographical knowledge graph and spatial reasoning
geographical process simulation
geographical behavior pattern recognition
and emotional computing. Finally
a case study of Chongqing based on the overall framework and technical system is conducted.
On the basis of “Chongqing 3D Space Digital Platform
” the corresponding practical results are presented from three levels of geographical ontology cognition
geographical process cognition
and geographical behavior cognition
displaying the use of relevant technical methods to recognize geographical environment and geospatial objects. Most existing studies focus on preliminary stages
such as the concept
research framework
expression of spatial objects
and geographical knowledge in the virtual environment. On the contrary
further exploration is conducted
thereby obtaining the actual cognitive results through the application of relevant technologies.
Under the support of overall framework and technical system
the realization approach and the practice results of geographical ontology cognition
geographical process cognition
and geographical behavior cognition are presented. This approach provides new ideas and solution for in-depth development and technical implementation of VGE- based geo-spatial cognition
and transforms the research from the conceptual discussion stage to the technical practice stage.
虚拟地理环境地理空间认知认知基本内容认知技术方法结构化语义建模城市计算地理知识图谱行为模式识别
Virtual Geographic Environmentsgeo-spatial cognitioncognition contentscognition techniquesstructured semantic modelingurban computinggeographic knowledge graphbehavior pattern recognition
Abayowa B O, Yilmaz A and Hardie R C. 2015. Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models. ISPRS Journal of Photogrammetry and Remote Sensing, 106: 68-81 [DOI: 10.1016/j.isprsjprs.2015.05.006http://dx.doi.org/10.1016/j.isprsjprs.2015.05.006]
Biljecki F, Ledoux H and Stoter J. 2017. Generating 3D city models without elevation data. Computers, Environment and Urban Systems, 64: 1-18 [DOI: 10.1016/j.compenvurbsys.2017.01.001http://dx.doi.org/10.1016/j.compenvurbsys.2017.01.001]
Biljecki F, Ledoux H, Stoter J and Vosselman G. 2016. The variants of an LOD of a 3D building model and their influence on spatial analyses. ISPRS Journal of Photogrammetry and Remote Sensing, 116: 42-54 [DOI: 10.1016/j.isprsjprs.2016.03.003http://dx.doi.org/10.1016/j.isprsjprs.2016.03.003]
Chai Y W, Ta N and Ma J. 2016. The socio-spatial dimension of behavior analysis: frontiers and progress in Chinese behavioral geography. Journal of Geographical Sciences, 26(8): 1243-1260 [DOI: 10.1007/s11442-016-1324-xhttp://dx.doi.org/10.1007/s11442-016-1324-x]
Chen C L P and Zhang C Y. 2014. Data-intensive applications, challenges, techniques and technologies: a survey on Big Data. Information Sciences, 275: 314-347 [DOI: 10.1016/j.ins.2014.01.015http://dx.doi.org/10.1016/j.ins.2014.01.015]
Chen Y F. 2001. Spatial cognition research on electronic maps. Progress in Geography, 20(S1): 63-68
陈毓芬. 2001. 电子地图的空间认知研究. 地理科学进展, 20(S1): 63-68 [DOI: 10.11820/dlkxjz.2001.s1.007http://dx.doi.org/10.11820/dlkxjz.2001.s1.007]
Guan S P, Jin X L, Jia Y T, Wang Y Z and Cheng X Q. 2018. Knowledge reasoning over knowledge graph: a survey. Journal of Software, 29(10): 2966-2994
官赛萍, 靳小龙, 贾岩涛, 王元卓, 程学旗. 2018. 面向知识图谱的知识推理研究进展. 软件学报, 29(10): 2966-2994 [DOI: 10.13328/j.cnki.jos.005551http://dx.doi.org/10.13328/j.cnki.jos.005551]
Guo H D. 2009. Digital Earth: ten years’ development and prospect. Advances in Earth Science, 24(9): 955-962
郭华东. 2009. 数字地球: 10年发展与前瞻. 地球科学进展, 24(9): 955-962 [DOI: 10.3321/j.issn:1001-8166.2009.09.001http://dx.doi.org/10.3321/j.issn:1001-8166.2009.09.001]
Guo H D and Yang C J. 1999. Developing national earth observing system for “Digital Earth”. Journal of Remote Sensing, 3(2): 90-93
郭华东, 杨崇俊. 1999. 建设国家对地观测体系, 构筑“数字地球”. 遥感学报, 3(2): 90-93
Hu Y J, Lv Z H, Wu J P, Janowicz K, Zhao X Z and Yu B L. 2015. A multistage collaborative 3D GIS to support public participation. International Journal of Digital Earth, 8(3): 212-234 [DOI: 10.1080/17538947.2013.866172http://dx.doi.org/10.1080/17538947.2013.866172]
Huang J, Levinson D, Wang J E, Zhou J P and Wang Z J. 2018. Tracking job and housing dynamics with smartcard data. Proceedings of the National Academy of Sciences of the United States of America, 115(50): 12710-12715 [DOI: 10.1073/pnas.1815928115http://dx.doi.org/10.1073/pnas.1815928115]
Jia F L, Zhang W W and You X. 2015. Cognitive research framework of virtual geographic environment. Journal of Remote Sensing, 19(2): 179-187
贾奋励, 张威巍, 游雄. 2015. 虚拟地理环境的认知研究框架初探. 遥感学报, 19(2): 179-187 [DOI: 10.11834/ jrs.20154013http://dx.doi.org/10.11834/jrs.20154013]
Jiang B C, Wan G, Xu J, Li F and Wen H Q. 2018. Geographic knowledge graph building extracted multi-sourced heterogeneous data. Acta Geodaetica et Cartographica Sinica, 47(8): 1051-1061
蒋秉川, 万刚, 许剑, 李锋, 温荟琦. 2018. 多源异构数据的大规模地理知识图谱构建. 测绘学报, 47(8): 1051-1061 [DOI: 10.11947/j.AGCS.2018.20180113http://dx.doi.org/10.11947/j.AGCS.2018.20180113]
Jiang N, Fang C and Chen M J. 2017. Initial exploration of pan-spatial cognition and representation. Journal of Geo-information Science, 19(9): 1150-1157
江南, 方成, 陈敏颉. 2017. 全空间信息系统认知与表达初探. 地球信息科学学报, 19(9): 1150-1157 [DOI: 10.3724/SP.J.1047.2017.01150http://dx.doi.org/10.3724/SP.J.1047.2017.01150]
Leotta M J, Long C J, Jacquet B, Zins M, Lipsa D, Shan J, Xu B, Li Z X, Zhang X, Chang S F, Purri M, Xue J and Dana K. 2019. Urban semantic 3D reconstruction from multiview satellite imagery//Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Long Beach, CA, USA: IEEE: 1451-1460 [DOI: 10.1109/CVPRW.2019.00186http://dx.doi.org/10.1109/CVPRW.2019.00186]
Li D R, Shao Z F, Yu W B, Zhu X Y and Zhou S H. 2020. Public epidemic prevention and control services based on big data of spatiotemporal location make cities more smart. Geomatics and Information Science of Wuhan University, 45(4): 475-487, 556
李德仁, 邵振峰, 于文博, 朱欣焰, 周素红. 2020. 基于时空位置大数据的公共疫情防控服务让城市更智慧. 武汉大学学报(信息科学版), 45(4): 475-487, 556 [DOI: 10.13203/j.whugis20200145http://dx.doi.org/10.13203/j.whugis20200145]
Li M F, Shi X, Li X, Ma W J, He J F and Liu T. 2019. Epidemic forest: a spatiotemporal model for communicable diseases. Annals of the American Association of Geographers, 109(3): 812-836 [DOI: 10.1080/24694452.2018.1511413http://dx.doi.org/10.1080/24694452.2018.1511413]
Li S and Yao J. 2005. The multi-dimension data model of virtual geographic environments and its expression in geographical processes. Geography and Geo-Information Science, 21(4): 1-5
李爽, 姚静. 2005. 虚拟地理环境的多维数据模型与地理过程表达. 地理与地理信息科学, 21(4): 1-5 [DOI: 10.3969/j.issn.1672-0504.2005.04.001http://dx.doi.org/10.3969/j.issn.1672-0504.2005.04.001]
Li Y Z, Fei T, Huang Y J, Li J, Li X, Zhang F, Kang Y H and Wu G F. 2021. Emotional habitat: mapping the global geographic distribution of human emotion with physical environmental factors using a species distribution model. International Journal of Geographical Information Science, 35(2): 227-249 [DOI: 10.1080/13658816.2020.1755040http://dx.doi.org/10.1080/13658816.2020.1755040]
Lin H, Chen M, Lu G N, Zhu Q, Gong J H, You X, Wen Y N, Xu B L and Hu M Y. 2013. Virtual Geographic Environments (VGEs): a new veneration of geographic analysis tool. Earth-Science Reviews, 126: 74-84 [DOI: 10.1016/j.earscirev.2013.08.001http://dx.doi.org/10.1016/j.earscirev.2013.08.001]
Lin H and Gong J H. 2002. On virtual geographic environments. Acta Geodaetica et Cartographica Sinica, 31(1): 1-6
林珲, 龚建华. 2002. 论虚拟地理环境. 测绘学报, 31(1): 1-6 [DOI: 10.3321/j.issn:1001-1595.2002.01.001http://dx.doi.org/10.3321/j.issn:1001-1595.2002.01.001]
Lin H, Hu M Y, Chen M, Zhang F, You L and Chen Y T. 2020. Cognitive transformation from geographic information system to virtual geographic environments. Journal of Geo-Information Science, 22(4): 662-672
林珲, 胡明远, 陈旻, 张帆, 游兰, 陈宇婷. 2020. 从地理信息系统到虚拟地理环境的认知转变. 地球信息科学学报, 22(4): 662-672 [DOI: 10.12082/dqxxkx.2020.200048http://dx.doi.org/10.12082/dqxxkx.2020.200048]
Lin H, Huang F R, Lu X J, Hu M Y, Xu B L and Wu L. 2010. Preliminary study on virtual geographic environment cognition and representation. Journal of Remote Sensing, 14(4): 822-838
林珲, 黄凤茹, 鲁学军, 胡明远, 徐丙立, 武磊. 2010. 虚拟地理环境认知与表达研究初步. 遥感学报, 14(4): 822-838 [DOI: 10.11834/jrs.20100415http://dx.doi.org/10.11834/jrs.20100415]
Lin H, Zhang C X, Chen M and Zheng X Q. 2016. On virtual geographic environments for geographic knowledge representation and sharing. Journal of Remote Sensing, 20(5): 1290-1298
林珲, 张春晓, 陈旻, 郑新奇. 2016. 论虚拟地理环境对地理知识的表达与共享. 遥感学报, 20(5): 1290-1298 [DOI: 10.11834/lrs.20166185http://dx.doi.org/10.11834/lrs.20166185]
Lin H and Zhu Q. 2005. The linguistic characteristics of virtual geographic environments. Journal of Remote Sensing, 9(2): 158-165
林珲, 朱庆. 2005. 虚拟地理环境的地理学语言特征. 遥感学报, 9(2): 158-165
Lin H, Zhu Q and Chen M. 2018. The being and non-being generate each other, and the virtual and the real are mutually interactive-the progress of Virtual Geographic Environments (VGE) studies in last 20 years. Acta Geodaetica et Cartographica Sinica, 47(8): 1027-1030
林珲, 朱庆, 陈旻. 2018. 有无相生虚实互济——虚拟地理环境研究20周年综述. 测绘学报, 47(8): 1027-1030 [DOI: 10.11947/j.AGCS.2018.20180202http://dx.doi.org/10.11947/j.AGCS.2018.20180202]
Liu F, Wang G X, Qian H Z, Hou X and Li K. 2009. The influences of virtual geographic environment on styles of spatial cognition. Science of Surveying and Mapping, 34(4): 67-69, 33
刘芳, 王光霞, 钱海忠, 侯璇, 李科. 2009. 虚拟地理环境对空间认知方式的影响. 测绘科学, 34(4): 67-69, 33
Liu H J and Li L. 2020. An accurate real-time virtual reality fusion method based on local acceleration. Engineering Journal of Wuhan University, 53(5): 442-446
刘华俊, 李黎. 2020. 一种基于局部加速的实时精确虚实融合方法. 武汉大学学报(工学版), 53(5): 442-446 [DOI: 10.14188/j.1671-8844.2020-05-010http://dx.doi.org/10.14188/j.1671-8844.2020-05-010]
Liu J N, Liu H Y, Chen X H, Guo X, Guo W Y, Zhu X M and Zhao Q B. 2020. The construction of knowledge graph towards multi-source geospatial data. Journal of Geo-Information Science, 22(7): 1476-1486
刘俊楠, 刘海砚, 陈晓慧, 郭漩, 郭文月, 朱新铭, 赵清波. 2020. 面向多源地理空间数据的知识图谱构建. 地球信息科学学报, 22(7): 1476-1486 [DOI: 10.12082/dqxxkx.2020.190565http://dx.doi.org/10.12082/dqxxkx.2020.190565]
Liu Y, Kang C G and Wang F H. 2014. Towards big data-driven human mobility patterns and models. Geomatics and Information Science of Wuhan University, 39(6): 660-666
刘瑜, 康朝贵, 王法辉. 2014. 大数据驱动的人类移动模式和模型研究. 武汉大学学报(信息科学版), 39(6): 660-666 [DOI: 10.13203/j.whugis20140149http://dx.doi.org/10.13203/j.whugis20140149]
Liu Y, Zhan Z H, Zhu D, Chai Y W, Ma X J and Wu L. 2018. Incorporating multi-source big geo-data to sense spatial heterogeneity patterns in an urban space. Geomatics and Information Science of Wuhan University, 43(3): 327-335
刘瑜, 詹朝晖, 朱递, 柴彦威, 马修军, 邬伦. 2018. 集成多源地理大数据感知城市空间分异格局. 武汉大学学报(信息科学版), 43(3): 327-335 [DOI: 10.13203/j.whugis20170383http://dx.doi.org/10.13203/j.whugis20170383]
Lu C H, Li H F, Gao T, Xu L and Li H L. 2019. Virtual reality head-mounted display with large field of view based on stitching. Acta Optica Sinica, 39(6): 0612002
陆驰豪, 李海峰, 高涛, 徐良, 李海丽. 2019. 基于拼接的大视场虚拟现实头戴显示装置. 光学学报, 39(6): 0612002 [DOI: 10.3788/AOS201939.0612002http://dx.doi.org/10.3788/AOS201939.0612002]
Lu F, Yu L and Qiu P Y. 2017. On geographic knowledge graph. Journal of Geo-Information Science, 19(6): 723-734
陆锋, 余丽, 仇培元. 2017. 论地理知识图谱. 地球信息科学学报, 19(6): 723-734 [DOI: 10.3969/j.issn.1560-8999.2017.06.001http://dx.doi.org/10.3969/j.issn.1560-8999.2017.06.001]
Lü G N, Yu Z Y, Zhou L C, Wu M G, Sheng Y H and Yuan L W. 2015. Data environment construction for virtual geographic environment. Environmental Earth Sciences, 74(10): 7003-7013 [DOI: 10.1007/s12665-015-4736-5http://dx.doi.org/10.1007/s12665-015-4736-5]
Ma H. 2019. Research on building and application of large-scale multi-source and multiscale real 3D model: a case study of Chongqing real 3D model building. Bulletin of Surveying and Mapping, (S2): 61-64 (马红. 2019. 大范围多源多尺度实景三维模型建设及应用研究——以重庆市实景三维模型建设为例. 测绘通报, (S2): 61-64) [DOI: 10.13474/j.cnki.11-2246.2019.0590]
Ni L L, Zhang S C and Chen X Q. 2017. Spatial effects of urban travel using cellular signaling data. Journal of Zhejiang University (Engineering Science), 51(5): 887-895
倪玲霖, 张帅超, 陈喜群. 2017. 基于手机信令数据的居民出行空间效应. 浙江大学学报(工学版), 51(5): 887-895 [DOI: 10.3785/j.issn.1008-973X.2017.05.007http://dx.doi.org/10.3785/j.issn.1008-973X.2017.05.007]
Qu Y C, Gao Z Y and Li X G. 2014. Modeling and simulating herding behavior and information spreading process in pedestrian flow. Journal of Transportation Systems Engineering and Information Technology, 14(5): 188-193
屈云超, 高自友, 李新刚. 2014. 考虑从众效应和信息传递的行人疏散建模. 交通运输系统工程与信息, 14(5): 188-193 [DOI: 10.16097/j.cnki.1009-6744.2014.05.063http://dx.doi.org/10.16097/j.cnki.1009-6744.2014.05.063]
Shaw S L and Fang Z X. 2014. Rethinking human behavior research from the perspective of space-time GIS. Geomatics and Information Science of Wuhan University, 39(6): 667-670
萧世伦, 方志祥. 2014. 从时空GIS视野来定量分析人类行为的思考. 武汉大学学报(信息科学版), 39(6): 667-670 [DOI: 10.13203/j.whugis20140127http://dx.doi.org/10.13203/j.whugis20140127]
Svoray T, Dorman M, Shahar G and Kloog I. 2018. Demonstrating the effect of exposure to nature on happy facial expressions via Flickr data: advantages of non-intrusive social network data analyses and geoinformatics methodologies. Journal of Environmental Psychology, 58: 93-100 [DOI: 10.1016/j.jenvp.2018.07.006http://dx.doi.org/10.1016/j.jenvp.2018.07.006]
Tomljenovic I, Höfle B, Tiede D and Blaschke T. 2015. Building extraction from airborne laser scanning data: an analysis of the state of the art. Remote Sensing, 7(4): 3826-3862 [DOI: 10.3390/rs70403826http://dx.doi.org/10.3390/rs70403826]
Torrens P M. 2015. Slipstreaming human geosimulation in virtual geographic environments. Annals of GIS, 21(4): 325-344 [DOI: 10.1080/19475683.2015.1009489http://dx.doi.org/10.1080/19475683.2015.1009489]
Torrens P M. 2018. A computational sandbox with human automata for exploring perceived egress safety in urban damage scenarios. International Journal of Digital Earth, 11(4): 369-396 [DOI: 10.1080/17538947.2017.1320594http://dx.doi.org/10.1080/17538947.2017.1320594]
Wan G, Gao J and Liu Y Z. 2008. Research on cognitive map formation based on reading experiments. Journal of Remote Sensing, 12(2): 339-346
万刚, 高俊, 刘颖真. 2008. 基于阅读实验方法的认知地图形成研究. 遥感学报, 12(2): 339-346
Wang S Y, Liu Y, Chen Z D, Shi L and Zhang J. 2018. Representing multiple urban places' footprints from Dianping.com Data. Acta Geodaetica et Cartographica Sinica, 47(8): 1105-1113
王圣音, 刘瑜, 陈泽东, 施力, 张晶. 2018. 大众点评数据下的城市场所范围感知方法. 测绘学报, 47(8): 1105-1113
Wang X, Chen J P, Fan H M, Li K, Zhang H and Zheng X. 2014. Lunar geological spatial data management system based on 3D WebGIS. Earth Science Frontiers, 21(6): 31-37
王翔, 陈建平, 范海明, 李珂, 章浩, 郑啸. 2014. 基于3D WebGIS月球地质空间数据管理系统. 地学前缘, 21(6): 31-37 [DOI: 10.13745/j.esf.2014.06.004http://dx.doi.org/10.13745/j.esf.2014.06.004]
Wu B, Xie L F, Hu H, Zhu Q and Yau E. 2018. Integration of aerial oblique imagery and terrestrial imagery for optimized 3D modeling in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 139: 119-132 [DOI: 10.1016/j.isprsjprs.2018.03.004http://dx.doi.org/10.1016/j.isprsjprs.2018.03.004]
Wu G Q, Dang A R, Tian Y and Kan C C. 2021. Study on the urban agglomerations structure of the Guangdong-Hong Kong-Macao Greater Bay Area based on spatiotemporal big data. National Remote Sensing Bulletin, 25(2): 665-676
吴冠秋, 党安荣, 田颖, 阚长城. 2021. 基于时空大数据的粤港澳大湾区城镇群结构研究. 遥感学报, 25(2): 665-676 [DOI：10.11834/jrs.20210590http://dx.doi.org/10.11834/jrs.20210590]
Wu J T, Leung K and Leung G M. 2020. Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study. The Lancet, 395(10225): 689-697 [DOI: 10.1016/S0140-6736(20)30260-9http://dx.doi.org/10.1016/S0140-6736(20)30260-9]
Xu J, Xu Y, Hu L and Wang Z B. 2020. Discovering spatio-temporal patterns of human activity on the Qinghai-Tibet Plateau based on crowdsourcing positioning data. Acta Geographica Sinica, 75(7): 1406-1417
许珺, 徐阳, 胡蕾, 王振波. 2020. 基于位置大数据的青藏高原人类活动时空模式. 地理学报, 75(7): 1406-1417 [DOI: 10.11821/dlxb202007006http://dx.doi.org/10.11821/dlxb202007006]
Zhang F, Hu M Y and Lin H. 2018. Virtual geographic cognition experiment in Big Data Era. Acta Geodaetica et Cartographica Sinica, 47(8): 1043-1050
张帆, 胡明远, 林珲. 2018. 大数据背景下的虚拟地理认知实验方法. 测绘学报, 47(8): 1043-1050 [DOI: 10.11947/j.AGCS.2018.20180103http://dx.doi.org/10.11947/j.AGCS.2018.20180103]
Zhang F and Liu Y. 2021. Street view imagery：Methods and applications based on artificial intelligence. NationalRemote Sensing Bulletin，25（5）：1043-1054
张帆, 刘瑜. 2021. 街景影像——基于人工智能的方法与应用. 遥感学报, 25(5): 1043-1054 [DOI：10.11834/jrs.20219341http://dx.doi.org/10.11834/jrs.20219341]
Zhang X Y, Zhang C J, Wu M G and Lü G N. 2020. Spatiotemporal features based geographical knowledge graph construction. Scientia Sinica Informationis, 50(7): 1019-1032
张雪英, 张春菊, 吴明光, 闾国年. 2020. 顾及时空特征的地理知识图谱构建方法. 中国科学: 信息科学, 50(7): 1019-1032 [DOI: 10.1360/SSI-2019-0269http://dx.doi.org/10.1360/SSI-2019-0269]
Zheng Y. 2015. Introduction to urban computing. Geomatics and Information Science of Wuhan University, 40(1): 1-13
郑宇. 2015. 城市计算概述. 武汉大学学报(信息科学版), 40(1): 1-13 [DOI: 10.13203/j.whugis20140718http://dx.doi.org/10.13203/j.whugis20140718]
Zhou C H. 2015. Prospects on pan-spatial information system. Progress in Geography, 34(2): 129-131
周成虎. 2015. 全空间地理信息系统展望. 地理科学进展, 34(2): 129-131 [DOI: 10.11820/dlkxjz.2015.02.001http://dx.doi.org/10.11820/dlkxjz.2015.02.001]
相关作者
相关机构
京公网安备11010802024621