On geospatial information science in the era of IoE

doi:10.11947/j.AGCS.2022.20210564

Abstract

Abstract: With the development of new technologies such as 5G/6G, cloud computing, internet of things and artificial intelligence, we have entered a new era of Internet of Everything(IoE). Five characteristics of geospatial information technology in the era of IoE discussed in this paper as followed:the development of satellite positioning technology has changed from GNSS and ground measurements to PNT system with wider service coverage; remote sensing technology has moved from isolated satellite observation to space-air-ground wireless sensor network; geographic information service moves from map database to true 3D realistic scene and digital twin; 3S integration develops from mobile measurement to intelligent robot service; research scope of geospatial information science changes from earth observation to internet of thing monitoring and perception of human activities in order to reveal the relationship between human and environment. On this basis, this paper further analyzes the challenges in the new era, and puts forward three major scientific issues to be solved:How can geographic information products meet the needs of people and robots at the same time? What are the mechanism and bottleneck of remote sensing image interpretation? How to use spatiotemporal big data to mine the relationship between man and nature and realize the leap from spatial perception to spatial cognition? Geospatial information science must and can make greater contributions to the digital earth and intelligent society in the era of IoE!

Key words: geospatial information science, internet of everything, spatial-temporal big data, cloud computing, artificial intelligence, spatial perception and cognition

CLC Number:

P208

LI Deren, XU Xiaodi, SHAO Zhenfeng. On geospatial information science in the era of IoE[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(1): 1-8.

References

[1] 李德仁. 展望大数据时代的地球空间信息学[J]. 测绘学报, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057. LI Deren. Towards geo-spatial information science in big data era[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(4):379-384. DOI:10.11947/j.AGCS.2016.20160057.
[2] 李德仁, 丁霖, 邵振峰. 面向实时应用的遥感服务技术[J]. 遥感学报, 2021, 25(1):15-24. LI Deren, DING Lin, SHAO Zhenfeng. Application-oriented real-time remote sensing service technology[J]. National Remote Sensing Bulletin, 2021, 25(1):15-24.
[3] GVNTHER O, MVLLER R. From GIS systems to GIServices:Spatial Computing on the Internet MarketplaceInteroperating Geographic Information Systems[M]. Berlin/Heidelberg/New York:Springer, 1999.
[4] YANG Yuanxi, LIU Li, LI Jinlong, et al. Featured services and performance of BDS-3[J]. Science Bulletin, 2021, 66(20):2135-2143.
[5] DING Qing, SHAO Zhenfeng, HUANG Xiao, et al. Monitoring, analyzing and predicting urban surface subsidence:a case study of Wuhan City, China[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 102:102422.
[6] CAI Bowen, SHAO Zhenfeng, FANG Shenghui, et al. Finer-scale spatiotemporal coupling coordination model between socioeconomic activity and eco-environment:a case study of Beijing, China[J]. Ecological Indicators, 2021, 131:108165.
[7] DOU Minggang, CHEN Jingying, CHEN Dan, et al. Modeling and simulation for natural disaster contingency planning driven by high-resolution remote sensing images[J]. Future Generation Computer Systems, 2014, 37:367-377.
[8] XU Xiaodi, TONG Ting, ZHANG Wen, et al. Fine-grained prediction of PM_2.5 concentration based on multisource data and deep learning[J]. Atmospheric Pollution Research, 2020, 11(10):1728-1737.
[9] 龚健雅,许越,胡翔云,等.遥感影像智能解译样本库现状与研究[J].测绘学报,2021,50(8):1013-1022. DOI:10.11947/j.AGCS.2021.20210085. GONG Jianya, XU Yue, HU Xiangyun, et al. Status analysis and research of sample database for intelligent interpretation of remote sensing image[J]. Acta Geodaetica et Cartographica Sinica,2021,50(8):1013-1022.DOI:10.11947/j.AGCS.2021.20210085.
[10] GONG Jianya, JI Shunping. Photogrammetry and deep learning[J]. Journal of Geodesy and Geoinformation Science, 2018, 1(1):1-15.
[11] MANDELBROT B. How long is the Coast of Britain? statistical self-similarity and fractional dimension[J]. Science, 1967, 156(3775):636-638.
[12] 李德仁. 地球空间信息学的使命[J]. 科技导报, 2011, 29(29):3. LI Deren. Mission of geospatial information science[J]. Science & Technology Review, 2011, 29(29):3.
[13] 胡焕庸.论中国人口之分布[M].北京:科学出版社,1983. Hu Huanyong. Distribution of China's population[M]. Beijing:Science Press, 1983.
[14] 杨元喜,杨诚,任夏.PNT智能服务[J].测绘学报,2021,50(8):1006-1012. DOI:10.11947/j.AGCS.2021.20210051. Yang Yuanxi, Yang Cheng, Ren Xia.PNT intelligent services[J].Acta Geodaetica et Cartographica Snicai, 2021,50(8):1006-1012.DOI:10.11947/j.AGCS.2021.20210051.
[15] 李德仁, 沈欣. 我国天基信息实时智能服务系统发展战略研究[J]. 中国工程科学, 2020, 22(2):138-143. LI Deren, SHEN Xin. Research on the development strategy of real time and intelligent space-based information service system in China[J]. Strategic Study of CAE, 2020, 22(2):138-143.
[16] 李德仁. 脑认知与空间认知:论空间大数据与人工智能的集成[J]. 武汉大学学报(信息科学版), 2018, 43(12):1761-1767. LI Deren. Brain cognition and spatial cognition:on integration of geo-spatial big data and artificial intelligence[J]. Geomatics and Information Science of Wuhan University, 2018, 43(12):1761-1767.
[17] 李德仁. 展望5G/6G时代的地球空间信息技术[J]. 测绘学报, 2019, 48(12):1475-1481. DOI:10.11947/j. AGCS.2019.20190437. LI Deren. Towards geospatial information technology in 5G/6G era[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(12):1475-1481. DOI:10.11947/j. AGCS.2019.20190437.
[18] 张广运, 张荣庭, 戴琼海, 等. 测绘地理信息与人工智能2.0融合发展的方向[J]. 测绘学报, 2021, 50(8):1096-1108. DOI:10.11947/j.AGCS.2021. 20210200. ZHANG Guangyun, ZHANG Rongting, DAI Qionghai, et al. The direction of integration surveying and mapping geographic information and artificial intelligence 2.0[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1096-1108. DOI:10.11947/j.AGCS.2021. 20210200.
[19] 闫利, 陈宇, 谢洪, 等. 测量机器人的关键技术[J]. 测绘学报, 2021, 50(9):1159-1169. DOI:10.11947/j.AGCS.2021.20210090. YAN Li, CHEN Yu, XIE Hong, et al. Surveying robot and its key technology[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(9):1159-1169.DOI:10.11947/j.AGCS.2021.20210090.
[20] DI Kaichang, WAN Wenhui, ZHAO Hongying, et al. Progress and applications of visual SLAM[J]. Journal of Geodesy and Geoinformation Science, 2019, 2(2):38-49.
[21] LI Xi, LI Deren. Can night-time light images play a role in evaluating the Syrian Crisis?[J]. International Journal of Remote Sensing, 2014, 35(18):6648-6661.
[22] SHUBINA V, HOLCER S, GOULD M, et al. Survey of decentralized solutions with mobile devices for user location tracking, proximity detection, and contact tracing in the COVID-19 era[J]. Data, 2020, 5(4):87.
[23] 王科俊, 赵彦东, 邢向磊. 深度学习在无人驾驶汽车领域应用的研究进展[J]. 智能系统学报, 2018, 13(1):55-69. WANG Kejun, ZHAO Yandong, XING Xianglei. Deep learning in driverless vehicles[J]. CAAI Transactions on Intelligent Systems, 2018, 13(1):55-69.
[24] 刘经南, 詹骄, 郭迟, 等. 智能高精地图数据逻辑结构与关键技术[J]. 测绘学报, 2019, 48(8):939-953. DOI:10.11947/j.AGCS.2019.20190125. LIU Jingnan, ZHAN Jiao, GUO Chi, et al. Data logic structure and key technologies on intelligent high-precision map[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(8):939-953. DOI:10.11947/j.AGCS.2019.20190125.
[25] 李德仁, 王树良, 李德毅. 空间数据挖掘理论与应用[M]. 2版. 北京:科学出版社, 2013. LI Deren, WANG Shuliang, LI Deyi. Spatial Data Mining Theories and Applications[M]. 2nd.Beijing:Science Press, 2013.
[26] 张继贤, 李海涛, 顾海燕, 等. 人机协同的自然资源要素智能提取方法[J]. 测绘学报, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102. ZHANG Jixian, LI Haitao, GU Haiyan, et al. Study on man-machine collaborative intelligent extraction for natural resource features[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(8):1023-1032. DOI:10.11947/j.AGCS.2021.20210102.