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SCIENTIA SINICA Informationis, Volume 47, Issue 2: 193-206(2017) https://doi.org/10.1360/N112016-00127

A new age of public-oriented Earth observation development}{A new age of public-oriented Earth observation development

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  • ReceivedMay 13, 2016
  • AcceptedDec 9, 2016
  • PublishedFeb 10, 2017

Abstract

Atmospheric, marine, and terrestrial Earth observations have been carried out by the government for half a century; their industrial characteristics notably benefit the public. The observation mode has been diversified, richer application methods have been introduced, especially the micro satellite cloud, and the observations and data-providing capabilities have been greatly improved in the past ten years due to the increasing number of satellites. Earth observations have entered the new era of big data and have become the main focus with respect to big scientific and consumption data. The industrial Earth observation development model is analyzed in this paper with respect to industrial ecological systems. The microsatellite cloud and big data technology are the two main forces of the shift of the development mode of Earth observations from public benefit-oriented to public-oriented. The study further analyzes the diverse effects of the public on Earth observation activities. We believe that the public will gradually become an active factor affecting all aspects of Earth observations. Meanwhile, public-oriented Earth observations target a bigger market. The public is both the main consumer and producer of Earth observation information. The public-oriented development model provides great opportunities for Earth observations and space-based information industry development; however, it also puts forward a series of scientific and technical challenges for the scientific community.


Funded by

中国科学院数字地球重点实验室主任基金 海南省重大科技计划项目(ZDKJ2016021)


References

[1] Gore A. The digital earth: understanding our planet in the 21st century. Australian Surveyor, 1998, 43: 89-91 CrossRef Google Scholar

[2] Chen S P, Guo H D. Digital earth and earth observation. Acta Geograph Sin, 2000, 55: 9-14 [陈述彭, 郭华东. ``数字地球"与对地观测. 地理学报, 2000, 55: 9-14]. Google Scholar

[3] Greenslade D, Berkhout F. Future earth -- research for global sustainability. In: Proceedings of EGU General Assembly Conference, Vienna, 2014. 312-316. Google Scholar

[4] Guo H D. Big data, big science, big discovery -- review of CODATA Workshop on Big Data for International Scientific Programmes. Bull Chin Acad Sci, 2014, 29: 500-506 [郭华东. 大数据 大科学 大发现---大数据与科学发现国际研讨会综述. 中国科学院院刊, 2014, 29: 500-506]. Google Scholar

[5] Frosch R A, Gallopoulos N E. Strategies for manufacturing. Sci American, 1989, 261: 144-152. Google Scholar

[6] Porter M E. The competitive advantage of nations. Harvard Bus Rev, 1990, 68: 73-93. Google Scholar

[7] Lohr S. The age of big data. New York Times, 2012, 11. Google Scholar

[8] Labrinidis A, Jagadish H V. Challenges and opportunities with big data. Proc VLDB Endowment, 2012, 5: 2032-2033 CrossRef Google Scholar

[9] Barwick H. The ``four vs" of big data. http://www.computerworld.com.au/article/396198/iiis\_four\_vs\_big\_data/, 2012. Google Scholar

[10] Mayer-Schönberger V, Cukier K. Big Data: A Revolution That Will Transform How We Live, Work, and Think. Boston: Houghton Mifflin Harcourt, 2013. 1-45. Google Scholar

[11] Niu S. Big data strategy and its influence in developed countries. International Study Reference, 2014, 27: 29-33 [牛帅. 发达国家大数据战略及其影响. 国际研究参考, 2014, 27: 29-33]. Google Scholar

[12] Pulse U N G. Big Data for Development: Challenges & Opportunities. New York: UN Global Pulse, 2012. Google Scholar

[13] Mundial F E. Big Data, Big Impact: New Possibilities for International Development. Cologny: Foro Económico Mundial, 2012. Google Scholar

[14] House W. Big Data: Seizing Opportunities, Preserving Values. Washington: Exceutive Office of the President, 2014. Google Scholar

[15] Dutta S, Geiger T, Lanvin B. The global information technology report 2015. In: Proceedings of World Economic Forum, Davos Klosters, 2015. 80-85. Google Scholar

[16] Tolle K M, Tansley D, Hey A J G. The fourth paradigm: data-intensive scientific discovery. General Collect, 2012, 99: 1334-1337. Google Scholar

[17] Reichman O J, Jones M B, Schildhauer M P. Challenges and opportunities of open data in ecology. Science, 2011, 331: 703-705 CrossRef Google Scholar

[18] Marx V. Biology: the big challenges of big data. Nature, 2013, 498: 255-260 CrossRef Google Scholar

[19] Manyika J, Chui M, Brown B, et al. Big Data: the Next Frontier for Innovation, Competition, and Productivity. Las Vegas: The McKinsey Global Institute, 2011. 10-16. Google Scholar

[20] Gantz J, Reinsel D. The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC iView: IDC Analyze the Future, 2012. 1-16. Google Scholar

[21] CODATA中国全国委员会. 大数据时代的科研活动. 北京: 科学出版社, 2014. 2-5. Google Scholar

[22] He G J, Wang L Z, Ma Y, et al. Processing of earth observation big data: challenges and countermeasures. Chin Sci Bull, 2015, 60: 470-478 [何国金, 王力哲, 马艳, 等. 对地观测大数据处理: 挑战与思考. 科学通报, 2015, 60: 470-478]. Google Scholar

[23] Guo H D, Wang L Z, Chen F, et al. Scientific big data and digital earth. Chin Sci Bull, 2014, 59: 1047-1054 [郭华东, 王力哲, 陈方, 等. 科学大数据与数字地球. 科学通报, 2014, 59: 1047-1054]. Google Scholar

[24] Song W J, Liu P, Wang L Z, et al. Intelligent processing of remote sensing big data: status and challenges. J Eng Studies, 2014, 6: 259-265 [宋维静, 刘鹏, 王力哲, 等. 遥感大数据的智能处理: 现状与挑战. 工程研究- 跨学科视野中的工程, 2014, 6: 259-265]. Google Scholar

[25] 邢月亭, 孙广勃, 范桃英. 美协会发表卫星产业状况报告. 中国航天, 2015, (8): 34-39. Google Scholar

[26] Casey K S. Big data for a big ocean at the NOAA national oceanographic data center. AGU Fall Meeting Abstracts, 2014, 1: 3621. Google Scholar

[27] Li D R, Zhang L P, Xia G S, et al. Automatic remote sensing data analysis and data mining. Acta Geod Cartogr Sin, 2014, 43: 1211-1216 [李德仁, 张良培, 夏桂松, 等. 遥感大数据自动分析与数据挖掘. 测绘学报, 2014, 43: 1211-1216]. Google Scholar

[28] Li D R, Tong Q X, Li R X, et al. Current issues in high-resolution earth observation technology. Sci China Earth Sci, 2012, 42: 805-813 [李德仁, 童庆禧, 李荣兴, 等. 高分辨率对地观测的若干前沿科学问题. 中国科学: 地球科学, 2012, 42: 805-813]. Google Scholar

[29] Quartulli M, Olaizola I G. A review of EO image information mining. Isprs J Photogramm Remote Sens, 2013, 75: 11-28 CrossRef Google Scholar

[30] 詹亚锋, 马正新, 曹志刚. 现代微小卫星技术及发展趋势. 电子学报, 2000, 28: 102-106. Google Scholar

[31] Buchen E, DePasquale D. 2014 Nano/Microsatellite Market Assessment. Atlanta: SpaceWorks Enterprises, Inc.(SEI), 2014. Google Scholar

[32] 贠敏. 小卫星, 大星座, 改变未来空间游戏规则---第三届小卫星技术交流会 (2015) 召开. 卫星应用, 2015, 6: 72-73. Google Scholar

[33] 陆文墨. 建立互联网+ 天基信息实施服务系统--- 李德仁院士谈航天与互联网+ 的融合. 卫星与网络, 2015, 6: 14-18. Google Scholar

[34] 黄立钠, 景育, 朱文杰, 等. 在轨补加技术在小卫星上的应用. 卫星与网络, 2015, 6: 72-75. Google Scholar

[35] Murthy K, Shearn M, Smiley B D, et al. SkySat-1: very high-resolution imagery from a small satellite. Proc SPIE 9241, Sensors, Systems, and Next-Generation Satellites XVIII, 2014, 9241: 92411E. Google Scholar

[36] Sandau R, Roeser H P, Valenzuela A. Small Satellite Missions for Earth Observation. Berlin: Springer, 2010. Google Scholar

[37] Rosenberg Z. The coming revolution in orbit. Foreign Policy, 2014, 205: 70. Google Scholar

[38] Goodchild M F. Citizens as sensors: the world of volunteered geography. Geo J, 2007, 69: 211-221. Google Scholar

[39] Fiorito S, Orsi F, Serdoura F M, et al. Data extraction from social networks for urban analyses. Sedimentology, 2011, 58: 579-617 CrossRef Google Scholar

[40] Howe J. Crowdsourcing: how the power of the crowd is driving the future of business. New York: Random House, 2008. 20-35. Google Scholar

[41] George C E, Scerri J. Web 2.0 and user-generated content: legal challenges in the new frontier. Social Sci Electron Publishing, 2007, 2: 1-22. Google Scholar

[42] Boulos M N K, Wheeler S. The emerging Web 2.0 social software: an enabling suite of sociable technologies in health and health care education. Health Inf Libr J, 2007, 24: 2-23. Google Scholar

[43] Daugherty T, Eastin M S, Bright L. Exploring consumer motivations for creating user-generated content. J Interactive Advertising, 2008, 8: 16-25 CrossRef Google Scholar

[44] Law E, Ahn L. Human Computation. Morgan & Claypool Publishers, 2011. 1-121. Google Scholar

[45] Law E, von Ahn L. Input-agreement: a new mechanism for collecting data using human computation games. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Boston, 2009. 1197-1206. Google Scholar

[46] von Ahn L. Games with a purpose. Computer, 2006, 39: 92-94. Google Scholar

[47] Silvertown J. A new dawn for citizen science. Trends Ecol Evol, 2009, 24: 467-471 CrossRef Google Scholar

[48] Wang F Y, Carley K M, Zeng D, et al. Social computing: from social informatics to social intelligence. IEEE Intell Syst, 2007, 22: 79-83. Google Scholar

[49] Parameswaran M, Whinston A B. Research issues in social computing. J Assoc Inf Syst, 2007, 8: 336-350. Google Scholar

[50] Satyanarayanan M. Pervasive computing: vision and challenges. IEEE Pers Commun, 2001, 8: 10-17. Google Scholar

[51] Lee M R, Lan Y. From Web 2.0 to conversational knowledge management: towards collaborative intelligence. J Entrep Res, 2007, 2: 47-62. Google Scholar

[52] Hackman J R. Collaborative Intelligence: Using Teams to Solve Hard Problems. Oakland: Berrett-Koehler Publishers, 2011. Google Scholar

[53] Nieto M J, Santamar\'{\i}a L. The importance of diverse collaborative networks for the novelty of product innovation. Technovation, 2007, 27: 367-377 CrossRef Google Scholar

[54] Cebon P. Swarm creativity: competitive advantage through collaborative innovation networks. Innov Manage Policy Practice, 2006, 8: 407-408. Google Scholar

[55] Bloom H K. Global Brain: the Evolution of the Mass Mind From the Big Bang to the 21st Century. Hoboken: Wiley, 2000. 114. Google Scholar

[56] Obermeyer N J. The evolution of public participation GIS. American Cartograph, 1998, 25: 65-66 CrossRef Google Scholar

[57] Sieber R. Public participation geographic information systems: a literature review and framework. Ann Assoc American Geograph, 2006, 96: 491-507 CrossRef Google Scholar

[58] Li W L. Community remote sensing: a new approach to geoscience applications. Remote Sens Land Resour, 2013, 25: 1-6 [李万伦. 社区遥感: 一种地学应用新技术. 国土资源遥感, 2013, 25: 1-6]. Google Scholar

[59] Zhao Y, Han Q. Spatial crowdsourcing: current state and future directions. IEEE Commun Mag, 2016, 54: 102-107. Google Scholar

[60] Kazemi L, Shahabi C. Geocrowd: enabling query answering with spatial crowdsourcing. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, Redondo Beach, 2012. 189-198. Google Scholar

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