Big Earth Data; Earth observation; scientific discoveries; decision support; sustainable development
Recent improvements in capability and ability of storing and utilizing vast quantity of data has enabled revolutionary innovation of big data analytics. Big data in just a span of few years have occupied strategic importance in many aspects of human society, making data an important commodity and a valuable resource in the era of knowledge based economies. Scientific research into big data collection, storage, analysis, and exploitation have developed rapidly and continues to progress at a rapid pace. At the same time, the amount of historical Earth observation data generated over the past five decades and the continued human and capital resource investments of many countries, public and private corporations ensures improved generation of Earth observation well into the future with exponentially increasing volumes of information on Earth systems and science. This has given rise to a new class of big data termed as "Big Earth Data". Big Earth Data has macrolevel capabilities that enable rapid and accurate monitoring of Earth, and is increasingly gaining importance in Earth sciences, adding value to its utilization in problem driven science and innovation. This paper introduces the characteristics of Big Earth Data and analyzes its great potential for development, particularly in regards to the role that Big Earth Data can play in transforming Earth science. With this context the paper outlines the Project on Big Earth Data Science Engineering (CASEarth) of the Chinese Academy of Sciences Strategic Priority Research Program and highlights how the CASEarth would contribute to ensure the actual use of Big Earth Data in support of the achievement of Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document. The potential prospects of developing Big Earth Data and its ability to integrate geosciences, information sciences, and space science and technology makes it a strategic future endeavor for revolutionizing Earth Science as a whole.
Bulletin of Chinese Academy of Sciences
郭华东, 陈润生, 徐志伟, 等.自然科学与人文科学大数据——第六届中德前沿探索圆桌会议综述.中国科学院院刊, 2016, 31(6):707-716.
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(4):500-506.
Guo H D, Wang L, Liang D. Big earth data from space:A new engine for earth science. Chinese Science Bulletin, 2016, 61(7):505-513.
Guo H D, Liu Z, Jiang H, et al. Big earth data:A new challenge and opportunity for digital earth's development. International Journal of Digital Earth, 2017, 10(1):1-12.
Vatsavai R R, Ganguly A, Chandola V, et al. Spatio temporal data mining in the era of big spatial data: algorithms and applications. ACM Sig spatial International Workshop on Analytics for Big Geospatial Data, 2012.
Reinsel D, Gantz J, Rydning J. Data age 2025: The evolution of data to life-critical don't focus on big data. Framingham: IDC Analyze the Future, 2017.
Guo H D. Steps to the digital Silk Road. Nature, 2018, 554:25-27.
He G, Wang L, Ma Y, et al. Processing of earth observation big data:Challenges and countermeasures. Chinese Science Bulletin, 2015, 60(5-6):470-478.
Ramapriyan H, Brennan J, Waler J, et al. Managing Big Data: NASA Tackles Complex Data Challenges. Earth Imaging Journal. [2013-10-18]. http://eijournal.com/print/articles/managingbigdata.
Pekel J, Cottam A, Gorelick N, et al. High-resolution mapping of global surface water and its long-term changes. Nature, 2016, 540:418-422.
Hansen M C, Potapov P V, Moore R, et al. High-resolution global maps of 21st-century forest cover change. Science, 2013, 342:850-853.
Guo H D, Fu W X, Li X W, et al. Research on global change scientific satellites. Science China Earth Sciences, 2014, 57(2):204-215.
郭华东, 陈方, 邱玉宝.全球空间对地观测五十年及中国的发展.中国科学院院刊, 2013, 28(Z1):7-16.
Guo H D. Big Earth data:A new frontier in Earth and information sciences. Big Earth Data, 2017, 1:4-20.
United Nations. Transforming our World: The 2030 Agenda for Sust ainabl e Developm ent. [2018-06-30]. https://sustainabledevelopment.un.org/post2015/transformingourworld/publication.
Anderson K, Ryan B, Sonntag W, et al. Earth observation in service of the 2030 Agenda for Sustainable Development. Geospatial Information Science, 2017, 20:77-96.
World Health Organization. Global Burden of Disease. [2018-06-30]. http://www.who.int/topics/global_burden_of_disease/en/.
Getting started with the Sustainable Development Goals: A guide for stakeholders. [2018-06-30]. http://unsdsn.org/wpcontent/uploads/2015/12/151211-getting-started-guide-FINALPDF-.pdf.
United Nations Statistics Division. The Sustainable Development Goals Report 2018. [2018-06-30]. https://unstats.un.org/sdgs/report/2018/.
United Nations. Report of the Inter-Agency and Expert Group on Sustainable Development Goal Indicators. New York:United Nations, 2016.
Mark E, Bengtsson M, Akenji L. An optimistic analysis of the means of implementation for Sustainable Development Goals:Thinking about goals as means. Sustainability, 2016, 8(9):962-985.
"A Project on Big Earth Data Science Engineering,"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 33
, Article 8.
Available at: https://bulletinofcas.researchcommons.org/journal/vol33/iss8/8