سابِق|تال

Three development stages of mobile geographic information system technology

  • role： First author第一作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：qiaoyy@aircas.ac.cn
  • Introduction：乔彦友，研究方向为地理信息系统的理论和应用。E-mail： qiaoyy@aircas.ac.cn
QIAO Yanyou,  
  • role： Corresponding author通信作者
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

  • Email：changyf@aircas.ac.cn
  • Introduction：常原飞，研究方向为地理信息系统的研发和应用。E-mail： changyf@aircas.ac.cn
CHANG Yuanfei*

ملخص

The development of Mobile Geographic Information System (MGIS), which is driven by the change of information technology, is clarified and divided into three eras in this work: embedded, mobile Internet, and intelligent Internet Of Things (IOT) era.At the end of 1990s, with the completion of Global Positioning System (GPS) deployment in the United States, the functions related to information acquisition of desktop GIS system were transplanted to Personal Digital Assistant (PDA) and other embedded devices to facilitate field data acquisition. MGIS entered the “embedded era” combined with GPS. In this period, the stand-alone version of MGIS has been successfully applied in the field data collection of land, forestry, surveying, mapping, and other industries. Although MGIS had some online functions at that time, the bandwidth of mobile network was not enough to support high-frequency network GIS services.MGIS has gradually entered the “mobile Internet era” with the rise of 3G/4G and other broadband mobile networks and the popularity of intelligent mobile terminals (especially Android mobile phones). In this period, the core module of MGIS was translated from Global Navigation Satellite System (GNSS) to wireless communication network. The most typical applications in this era are the map APP developed by Google, Baidu, and other electronic map service providers and its related location-based services APP. At this time, MGIS has extended to the entire geographic information industry chains, involving data collection, data processing, platform software, industry applications, etc. “Cloud+End” constitutes a new ecosystem of geographic information. However, due to the problems of cloud computing, such as less real-time, insufficient bandwidth, and large energy consumption, which are not conducive to data security and privacy, MGIS in this era is still in the traditional artificial ground operation stage, making real-time spatial analysis, target recognition, and other intelligent processing difficult.The MGIS technology gradually enters the “intelligent IOT era” around 2019 with the ubiquitous development of IOT, especially the development of Computer Vision (CV), Artificial Intelligence (AI), 5G mobile communication, edge computing, and other technologies. The main technical features of this stage are intelligent, real-time, and ubiquitous GIS, and the system architecture evolves into “cloud+edge+end”. In this era, everyone is a sensor and plotter. A large number of intelligent sensors, such as cameras and radars integrated on ground mobile platforms (wearable devices, vehicles, etc.) and air mobile platforms (unmanned aerial vehicles, etc.) have emerged, which can help us locate and provide holographic map information, such as acoustic-optic-magnetic information. These IOT terminals can be used as carriers of MGIS. The massive raw data of the collected images, videos, locations, and other data are no longer uploaded to the cloud, but are analyzed and processed in real time by AI and other technologies on the network edge devices. Only the results are transmitted to the cloud. This mode greatly reduces the pressure of network bandwidth, data center power consumption, and system delay and enhances the service response ability. Furthermore, the risk of network data leakage is greatly reduced, and user data security and privacy are protected because users no longer upload privacy or sensitive data (only stored on network edge devices).On this basis, a new generation of MGIS is proposed, which integrates GNSS, 5G, AI, CV, and other information technologies. The ubiquitous, real-time, and intelligent features of this technology are analyzed, and three core technologies, including cross-platform kernel, simultaneous localization and mapping, pan-information-based high-precision navigation map, semantic map, and intelligent decision-making, are discussed. The development trend and direction are also predicted.

مفهوم

mobile GIS;artificial intelligence;UAV remote sensing;internet of things;cloud computing;edge computing;smart industry

References

  1. 1.
    Bailey T and Durrant-Whyte H. 2006. Simultaneous localization and mapping (SLAM): part Ⅱ. IEEE Robotics and Automation Magazine, 13(3): 108-117
  2. 2.
    Bu J and Zhang Q. 2004. Technical implementation of distributed mobile GIS based on LBS. Journal of Chongqing University of Posts and Telecommunications, 16(1): 105-107
  3. 3.
    Chang Y F, Wu H G, Dong Z H, Cui F D, Liu L, Jian J, Zhang Y and Li W G. 2011. A national monitoring and warning system for forest pest based on service-oriented architecture. Scientia Silvae Sinicae, 47(6): 93-100
  4. 4.
    Che Z L. 2016. Study on Techniques of Disaster Point Investigation for Forest Pests Based on Location. Beijing: University of Chinese Academy of Sciences
  5. 5.
    Chen F X. 2006. Research on Key Technologies for Mobile Spatial Information Services. Beijing: Institute of Remote Sensing Applications Chinese Academy of Sciences
  6. 6.
    Chen Z J, Sun E X, Li D D, Zhang C C, Zang D D and Cheng X. 2018. Current Situation Analysis and Implementation of High Precision Map. Computer Knowledge and Technology, 14(22): 270-272
  7. 7.
    Cui F D. 2009. Research on Management Technology of Forestry Pest Web Services. Beijing: Institute of Remote Sensing Application Chinese Academy of Sciences
  8. 8.
    Durrant-Whyte H and Bailey T. 2006. Simultaneous localization and mapping: part Ⅰ. IEEE Robotics and Automation Magazine, 13(2): 99-110
  9. 9.
    Girshick R, Donahue J, Darrell T and Malik J. 2016. Region-based convolutional networks for accurate object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1): 142-158
  10. 10.
    Gong J Y. 2004. Review of the progress in contemporary GIS. Geomatics and Spatial Information Technology, 27(1): 5-11
  11. 11.
    He D. 2008. Research and Application of Embedded GIS in Forest Pest Investigation. Beijing: Institute of Remote Sensing Application Chinese Academy of Sciences
  12. 12.
    He K M, Gkioxari G, Dollár P and Girshick R. 2020. Mask R-CNN. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2): 386-397
  13. 13.
    Jian J. 2005. Urban Fundamental Geographical Information Platform Framework and Key Technology Study. Beijing: Graduate School of the Chinese Academy of Sciences (Institute of Remote Sensing Application)
  14. 14.
    Lee N S T. 1993. Single line street network: the foundation of mobile GIS//Vehicle Navigation and Information Systems Conference. Ottawa, Ontario: IEEE: 34-37
  15. 15.
    Li C M, Wang J Z and Liu Y. 2004. Theory and application of moving GIS. Geomatics and Information Science of Wuhan University, 29(11): 990-993
  16. 16.
    Li D R. 2008. Mobile mapping technology and its applications//Proceedings of the 4th Council of the 9th CSMS and its 2008 Annual Meeting. Guilin: 638-654
  17. 17.
    Li H Y. 2006. Research on the Concept System of Mobile GIS. Xi’an: Chang’an University
  18. 18.
    Li W G. 2010. Research on Field Investigation System Based on Embedded GIS. Beijing: Institute of Remote Sensing Application Chinese Academy of Sciences
  19. 19.
    Liao X H, Xiao Q and Zhang H. 2019. UAV remote sensing: Popularization and expand application development trend. Journal of Remote Sensing, 23(6): 1046-1052
  20. 20.
    Liu H X, Liu H D, He H J and Zhao Y L. 2005. Development and application of mobile GIS. Geospatial Information, 3(4): 41-42
  21. 21.
    Liu J J, Zou S H and Lu X L. 2021. Joint optimization scheme of resource allocation and offloading decision in mobile edge computing. Journal of Frontiers of Computer Science and Technology, 15(5): 848-858
  22. 22.
    Liu J N, Wu H B, Guo C, Zhang H M, Zuo W W and Yang C. 2018. Progress and consideration of high precision road navigation map. Strategic Study of CAE, 20(2): 99-105
  23. 23.
    Liu L. 2012. Research on General Framework and Template of Management Information System based on WebGIS. Beijing: Institute of Remote Sensing Application Chinese Academy of Sciences
  24. 24.
    Lu Y F, Cao R H, Wang J L and Yan C G. 2021. Method of encapsulating procuratorate affair services based on microservices. Computer Science, 48(2): 33-40
  25. 25.
    McCormac J, Handa A, Davison A and Leutenegger S. 2017. SemanticFusion: dense 3D semantic mapping with convolutional neural networks//2017 IEEE International Conference on Robotics and Automation. Singapore: IEEE: 4628-4635
  26. 26.
    Mukherjee M, Shu L and Wang D. 2018. Survey of fog computing: fundamental, network applications, and research challenges. IEEE Communications Surveys and Tutorials, 20(3): 1826-1857
  27. 27.
    Qian C, Liu H, Tang J, Chen Y W, Kaartinen H, Kukko A, Zhu L L, Liang X L, Chen L and Hyyppä J. 2017. An integrated GNSS/INS/LiDAR-SLAM positioning method for highly accurate forest stem mapping. Remote Sensing, 9(1): 3
  28. 28.
    Qiao Y Y, Chang Y F, He D, Zhang Y and Jian J. 2009. GIS/GPS-based forestry pest monitoring data recording system and its industrialization. Journal of Remote Sensing, 13(S1): 422-426
  29. 29.
    Redmon J, Divvala S, Girshick R and Farhadi A. 2016. You only look once: unified, real-time object detection//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV: IEEE: 779-788
  30. 30.
    Ren S Q, He K M, Girshick R and Sun J. 2017. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6): 1137-1149
  31. 31.
    Si J Z. 2012. Research of Field Forest Data Capture Technology based on GIS/GPS. Beijing: University of Chinese Academy of Sciences
  32. 32.
    Tian G, Zhang J and Tong X H. 2004. Research and implementation of embedded GIS based on mobile. Remote Sensing Information, (1): 49-52
  33. 33.
    Wang C B. 2015. Study on Automatic Recognition and Service-Oriented Informationized Field Investigation of Discolored Pine Trees. Beijing: Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences (王成波. 2015. 变色松树的图像识别与地面调查综合技术研究. 北京: 中国科学院遥感与数字地球研究所)
  34. 34.
    Wang C B, Qiao Y Y, Wu H G, Chang Y F and Shi M Y. 2016. Empowering fall webworm surveillance with mobile phone-based community monitoring: a case study in northern China. Journal of Forestry Research, 27(6): 1407-1414
  35. 35.
    Wang D, Li X and Zhu M Z. 2006. Research on system architecture of mobile GIS. Computer Engineering and Design, 27(20): 3835-3837
  36. 36.
    Wang G and Han Z B. 2013. Design and implementation of android-oriented mobile GIS. Bulletin of Surveying and Mapping, (8): 77-80
  37. 37.
    Wang J Z and Li M C. 2005. Research on embedded mobile GIS. Science of Surveying and Mapping, 30(4): 48-50
  38. 38.
    Wang P. 2006. Mobile GIS and its new applications. Geomatics and Spatial Information Technology, 29(5): 129-131
  39. 39.
    Wang X B and Sun J Y. 2012. Geographic information system overview. Geospatial Information, 10(1): 25-28, 1
  40. 40.
    Whelan T, Salas-Moreno R F, Glocker B, Davison A J and Leutenegger S. 2016. ElasticFusion: real-time dense SLAM and light source estimation. The International Journal of Robotics Research, 35(14): 1697-1716
  41. 41.
    Wu H G, Chang Y F and He D. 2007. GIS/GPS-based Forestry Pest Monitoring Data Recording and Processing System. Forestry of China, (18): 54
  42. 42.
    Wu H G, Wang C B and Chang Y F. 2019. Study on cloud service management system of trees attacked by pine wood nematode disease. Forest Resources Management, (5): 68-75
  43. 43.
    Xia H. 2015. A Study on Context-Awareness of Mobile Device for Location-Based Service. Beijing: University of Chinese Academy of Sciences
  44. 44.
    Xia H, Zuo J B, Liu S and Qiao Y Y. 2019. Indoor localization on smartphones using built-In sensors and map constraints. IEEE Transactions on Instrumentation and Measurement, 68(4): 1189-1198
  45. 45.
    Xu Y Q, Huang H and Jin Z. 2021. Application research on container technology in scientific computing. Computer Science, 48(1): 319-325
  46. 46.
    Yao H M, Feng F and Chen J H. 2022. Practical application of vehicle-road cooperation based on high precision map and multi-sensor fusion positioning. Journal of Geomatics, 47(3): 68-69
  47. 47.
    Yao X H and Tan X Y. 2021. Reward highway network based global credit assignment algorithm in multi-agent reinforcement learning. Journal of Computer Applications, 41(1): 1-7
  48. 48.
    Yu Z Y, Lü G N, Zhang X N, Jia Y X, Zhou C H, Ge Y and Lü K J. 2020. Pan-information-based high precision navigation map: concept and theoretical model. Journal of Geo-Information Science, 22(4): 760-771
  49. 49.
    Zhang F and Liu Y. 2021. Street view imagery: methods and applications based on artificial intelligence. National Remote Sensing Bulletin， 25（5）：1043-1054
  50. 50.
    Zhang J J, Zhang H Y and Luo R. 2013. Research of mobile GIS based on Android platform and its implementation. Computer Engineering and Design, 34(9): 3322-3326
  51. 51.
    Zhang J X，Liu F and Wang J. 2021. Review of the light-weighted and small UAV system for aerial photography and remote sensing. National Remote Sensing Bulletin， 25（3）：708-724
  52. 52.
    Zhang S H, Fang Y. 2001. Development and significance of micro-embedded GIS software. Journal of Image and Graphics, 6A(9): 900-906
  53. 53.
    Zhang Y. 2007. Research and Application of County-Level Forestry Pest Monitoring and Management Information System based on GIS. Shijiazhuang: Hebei Normal University
  54. 54.
    Zhang Y. 2008. Research on the Spatial Data Service based Forest Pest Web Management Information System. Beijing: Institute of Remote Sensing Application Chinese Academy of Sciences
  55. 55.
    Zhang Z，Dang A R，Hou M L，Wu D F，Wang Z N，Zhang Z W and Xin T Q. 2021. Information technology methodology of the protection and utilization of the Great Wall Cultural Heritage System. National Remote Sensing Bulletin， 25(12)：2339-2350
  56. 56.
    Zhao W B and Zhang D R. 2003. Geographical information system in mobile computing environment. Geography and Geo -Information Science, 19(2): 19-23
  57. 57.
    Zhou C H. 2014. The Era of Holographic Map Has Come: Historical Evolution of Map Functions. Science of Surveying and Mapping, 39(7): 3-8
  58. 58.
    Zhou C H, Zhu X Y, Wang M, Shi C and Ou Y. 2011. Panoramic location-based map. Progress in Geography, 30(11): 1331-1335
  59. 59.
    Zhou T and Long Y. 2012. Review about recently development of mobile map and internet map in China. Geography and Geo-Information Science, 28(5): 1-5
  60. 60.
    Zou B, Lin S Y and Yin Z S. 2020. Semantic mapping based on YOLOv3 and visual SLAM. Laser and Optoelectronics Progress, 57(20):116-122
  61. 61.
    Zuo J B. 2018. Research on Mobile Perception of Urban Environment. Beijing: Institute of Remote Sensing and Digital Earth Chinese Academy of Sciences

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