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Big Earth Data from space: a new engine for Earth science

空间地球大数据:地球科学研究新引擎

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  • Earth Sciences
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Science Bulletin

Abstract

Big data is a strategic highland in the era of knowledge-driven economies, and it is also a new type of strategic resource for all nations. Big data collected from space for Earth observation—so-called Big Earth Data—is creating new opportunities for the Earth sciences and revolutionizing the innovation of methodologies and thought patterns. It has potential to advance in-depth development of Earth sciences and bring more exciting scientific discoveries. The Academic Divisions of the Chinese Academy of Sciences Forum on Frontiers of Science and Technology for Big Earth Data from Space was held in Beijing in June of 2015. The forum analyzed the development of Earth observation technology and big data, explored the concepts and scientific connotations of Big Earth Data from space, discussed the correlation between Big Earth Data and Digital Earth, and dissected the potential of Big Earth Data from space to promote scientific discovery in the Earth sciences, especially concerning global changes.

摘要

大数据是知识经济时代的战略高地,是国家和全球的新型战略资源。作为思维与方法论的创新与革命,空间地球大数据为地球科学研究带来了新机遇,有望为推动地球科学深度发展并产出重大科学发现做出贡献。中国科学院学部“空间地球大数据”科学与技术前沿论坛于2015年6月在北京召开。本次论坛剖析了空间对地观测技术及其大数据的发展,探讨了空间地球大数据理念,剖析了空间地球大数据科学内涵,讨论了空间地球大数据与数字地球关系,分析了空间地球大数据对推动地球系统科学及全球变化发展的潜力。

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References

  1. Turner V, Gantz JF, Reinsel D et al (2014) The digital universe of opportunities: rich data and the increasing value of the internet of things. IDC Analyze the Future, Framingham

    Google Scholar 

  2. Gantz J, Reinsel D (2012) The digital universe in 2020: big data, bigger digital shadows, and biggest growth in the far east. IDC Analyze the Future, Framingham

    Google Scholar 

  3. Fang JQ (2013) Network science and engineering faced with a new challenge and developing opportunity under the wave impact of big data. Chin J Nat 35:345–354 (in Chinese)

    Google Scholar 

  4. Science and Technology Commission of Shanghai Municipality (2014) Research on the development of science and technology 365(11): tracking the trend of big data technology development. http://images.stcsm.gov.cn/CMSstcsm/201405/201405220101004.pdf. Accessed 01 Aug 2015 (in Chinese)

  5. CEOS (2012) The earth observation handbook. Symbios Spazio Ltd., Horsham

    Google Scholar 

  6. Guo HD et al (2014) Scientific satellites for global change research. Science Press, Beijing (in Chinese)

    Google Scholar 

  7. He GJ, Wang LZ, Ma Y et al (2015) Processing of earth observation big data: challenges and countermeasures. Chin Sci Bull 60:470–478 (in Chinese)

    Article  Google Scholar 

  8. Hey T, Tansley S, Tolle K (2009) The fourth paradigm: data-intensive scientific discovery. Microsoft Research, Washington

    Google Scholar 

  9. Li DR, Zhang LP, Xia GS (2014) Automatic analysis and mining of remote sensing big data. Acta Geodaet Cartogr Sin 43:1211–1216 (in Chinese)

    Google Scholar 

  10. Shekhar S, Gunturi V, Evans MR et al (2012) Spatial big-data challenges intersecting mobility and cloud computing. In: Proceeding MobiDE ‘12 proceedings of the eleventh ACM international workshop on data engineering for wireless and mobile access’, pp 1–6

  11. Li QQ, Li DR (2014) Big data GIS. Geomat Inf Sci Wuhan Univ 39:641–644 (in Chinese)

    Google Scholar 

  12. Li DR, Yao Y, Shao ZF (2014) Big data in smart city. Geomat Inf Sci Wuhan Univ 39:631–640

    Google Scholar 

  13. Lv XF, Cheng CQ, Gong JY et al (2011) Review of data storage and management technologies for massive remote sensing data. Sci China Technol Sci 54:3220–3232

    Article  Google Scholar 

  14. Guo W, Gong JY, Jiang WS et al (2010) OpenRS-Cloud: a remote sensing image processing platform based on cloud computing environment. Sci China Technol Sci 53:221–230

    Article  Google Scholar 

  15. Huang QY, Yang CW, Nebert D et al (2010) Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon’s EC2. In: Proceedings of the ACM SIGSPATIAL international workshop on high performance and distributed geographic information systems, New York, USA, pp 35–38

  16. Martino SD, Bimonte S, Bertolotto M et al (2011) Spatial online analytical processing of geographic data through the Google Earth interface. In: Murgante B, Borruso G, Lapucci A (eds) Geocomputation, sustainability and environmental planning. Springer, Berlin, pp 163–182

    Chapter  Google Scholar 

  17. Li DR (2012) On space-air-ground integrated earth observation network. J Geo Inf Sci 14:419–425

    Google Scholar 

  18. Li DR, Zhang LP, Xia GS (2014) Automatic analysis and mining of remote sensing big data. Acta Geodaet Cartogr Sin 43:1211–1216 (in Chinese)

    Google Scholar 

  19. Vidal R, Ma Y, Sastry S (2005) Generalized principal component analysis (GPCA). IEEE Trans Pattern Anal Mach Intell 27:1945–1959

    Article  Google Scholar 

  20. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing of over complete dictionaries for sparse representation. IEEE Trans Signal Process 54:4311–4322

    Article  Google Scholar 

  21. Wang LZ, Lu K, Liu P et al (2014) IK-SVD: dictionary learning for spatial big data via incremental atom update. Comput Sci Eng 16:41–52

    Article  Google Scholar 

  22. Figueiredo MAT, Nowak RD, Wright SJ (2007) Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems. IEEE J Sel Top Signal Process 1:586–597

    Article  Google Scholar 

  23. Daubechies I, Devore R, Fornasier M et al (2010) Iteratively reweighted least squares minimization for sparse recovery. Commun Pure Appl Math 63:1–38

    Article  Google Scholar 

  24. Meenakshi N, Karthik G, Amit P et al (2009) Spatio-temporal-thematic analysis of citizen sensor data: challenges and experiences. In: Proceeding of web information system engineering, pp 539–554

  25. Vatsavai RR, Ganguly A, Chandola V et al (2012) Spatiotemporal data mining in the era of big spatial data: algorithms and applications. In: Proceedings of the 1st ACM SIGSPATIAL international workshop on analytics for big geospatial data. ACM, pp 1–10

  26. Ouyang Y, Zhang JE, Luo SM (2007) Dynamic data driven application system: recent development and future perspective. Ecol Model 204(1–2):1–8

    Article  Google Scholar 

  27. Guo HD, Liu LY, Lei LP et al (2010) Dynamic analysis of the Wenchuan Earthquake disaster and reconstruction with 3-year remote sensing data. Int J Digit Earth 3:355–364

    Article  Google Scholar 

  28. Graham M, Shelton T (2013) Geography and the future of big data, big data and the future of geography. Dialogues Hum Geogr 3:255–261

    Article  Google Scholar 

  29. Grossner KE, Goodchild MF, Clarke KC (2008) Defining a digital earth system. Trans GIS 12:145–160

    Article  Google Scholar 

  30. Guo HD (2012) China’s earth observing satellites for building a digital earth. Int J Digit Earth 5:185–188

    Article  Google Scholar 

  31. Guo HD (2015) Big data for scientific research and discovery. Int J Digit Earth 8:1–2

    Article  Google Scholar 

  32. Chen SP (2007) Ge-information science. Higher Education Press, Beijing (in Chinese)

    Google Scholar 

  33. Liao K, Qin JX, Zhang QN (2001) On geo-informatic tupu and digital earth. Geogr Res 20:55–61 (in Chinese)

    Google Scholar 

  34. Guo HD, Wang LZ, Chen F et al (2014) Scientific big data and digital earth. Chin Sci Bull 59:5066–5073

    Article  Google Scholar 

  35. Xu GH, Ge QS, Gong P et al (2013) Societal response to challenges of global change and human sustainable development. Chin Sci Bull 58:3161–3168

    Article  Google Scholar 

Download references

Acknowledgments

This work was supported by the Academic Divisions of the Chinese Academy of Sciences Forum on Frontiers of Science and Technology for Big Earth Data from Space.

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Correspondence to Huadong Guo.

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Guo, H., Wang, L. & Liang, D. Big Earth Data from space: a new engine for Earth science. Sci. Bull. 61, 505–513 (2016). https://doi.org/10.1007/s11434-016-1041-y

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  • DOI: https://doi.org/10.1007/s11434-016-1041-y

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