TY - JOUR
T1 - Big Earth data analytics
T2 - a survey
AU - Yang, Chaowei
AU - Yu, Manzhu
AU - Li, Yun
AU - Hu, Fei
AU - Jiang, Yongyao
AU - Liu, Qian
AU - Sha, Dexuan
AU - Xu, Mengchao
AU - Gu, Juan
N1 - Funding Information:
This work was supported by the National Science Foundation [OAC-1835507 and IIP-1841520].
Funding Information:
Various research and development efforts have been made to tackle the challenges brought by storing, transmitting, processing, analyzing, managing, and sharing big Earth data. As one of the most prominent systems, for example, Google Earth Engine is a cloud-based platform for planetary-scale geospatial analysis supported by Google’s cloud infrastructure (Gorelick et al., ). ArcGIS GeoAnalytics Server was released in 2016 to allow users to perform feature analysis using distributed computing (Wright et al., ). The Science Data Analytics Platform (SDAP), originally funded by National Aeronautics and Space Administration (NASA), was recently launched as an Apache Incubator project, the goal of which is to accelerate the study of the Earth’s physical oceanography (Apache, ). On top of SDAP, many applications have been developed including the State of the Ocean (SOTO) (PO.DAAC, ) and the Sea Level Portal (NASA, ). The planetary defense framework gateway is developed to support the decision-making on mitigating Near-Earth-Object’s impact on the Earth (Yang et al., ). The Chinese Academy of Science also funded a project to integrate Earth science data for addressing Earth science challenges and grand applications (Guo, ). In the public health domain, a big Earth data analytics framework is proposed to make better-informed health-related decisions by integrating big Earth data and health data (Raghupathi & Raghupathi, ). Focused on social media data, the Harvard Center for Geographic Analysis (CGA) developed a big spatiotemporal data visualization platform, the Billion Object Platform, with the purpose of lowering barriers for scholars who wish to access large, streaming, spatiotemporal data about the Earth surface (Kakkar, Lewis, Smiley, & Nunez, ).
Publisher Copyright:
© 2019 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth, supported by the CASEarth Strategic Priority Research Programme.
PY - 2019/4/3
Y1 - 2019/4/3
N2 - Big Earth data are produced from satellite observations, Internet-of-Things, model simulations, and other sources. The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding, responding, and addressing challenges of Earth sciences and applications. In the past years, new technologies (such as cloud computing, big data and artificial intelligence) have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable. This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain. We first introduce the concepts of big Earth data. The architecture, various functionalities, and supporting modules are then reviewed from a generic methodology aspect. Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools. The driven questions are exemplified through cutting-edge Earth science researches and applications. A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.
AB - Big Earth data are produced from satellite observations, Internet-of-Things, model simulations, and other sources. The data embed unprecedented insights and spatiotemporal stamps of relevant Earth phenomena for improving our understanding, responding, and addressing challenges of Earth sciences and applications. In the past years, new technologies (such as cloud computing, big data and artificial intelligence) have gained momentum in addressing the challenges of using big Earth data for scientific studies and geospatial applications historically intractable. This paper reviews the big Earth data analytics from several aspects to capture the latest advancements in this fast-growing domain. We first introduce the concepts of big Earth data. The architecture, various functionalities, and supporting modules are then reviewed from a generic methodology aspect. Analytical methods supporting the functionalities are surveyed and analyzed in the context of different tools. The driven questions are exemplified through cutting-edge Earth science researches and applications. A list of challenges and opportunities are proposed for different stakeholders to collaboratively advance big Earth data analytics in the near future.
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U2 - 10.1080/20964471.2019.1611175
DO - 10.1080/20964471.2019.1611175
M3 - Article
AN - SCOPUS:85074632171
SN - 2096-4471
VL - 3
SP - 83
EP - 107
JO - Big Earth Data
JF - Big Earth Data
IS - 2
ER -