2018 International Workshop on Big Geospatial Data and Data Science (BGDDS 2018)
The 2018 International Workshop on Big Geospatial Data and Data Science (BGDDS 2018) will be held in Wuhan, China on September 22-23. This conference is co-organized by Wuhan University, IEEE Geoscience and Remote Sensing Society (GRSS) Earth Science Informatics Technical Committee (ESI TC) and Open Geospatial Consortium (OGC) China Forum, and hosted by School of Remote Sensing and Information Engineering, Wuhan University as well as Hubei Province Engineering Center for Intelligent Geoprocessing.
Over the past decade, the Earth observing data managed and processed by information systems have increased from the terabyte level to the petabyte and exabyte levels. The rapid development of sensor and cyberinfrastructure technologies makes Earth observing (EO) data, which are generated by global and local sensor systems/networks measuring the state of Earth, an important part of Big Data. The data are not only bigger than before, but also have increased complexity due to their very special characteristics of volume, variety, velocity, value, veracity, and variability. The big EO data means that capabilities of traditional data systems and computational methods are inadequate to deal with these characteristics. Today, in addition to analysis of EO data only, Earth scientists are also using social and economic data to complement EO data to gain a better understanding of the social-economic-environmental systems. Infrastructure-based researches are being leveraged to enable fast analysis of the data.
The trends on big EO data lead to some questions that the Earth science community needs to address. Are we experiencing a paradigm shift in Earth science research now? How can we better utilize the explosion of technology maturation to create new forms of Earth observing data processing? Can we summarize the existing methodologies and technologies scaling to big EO data as a new field named "Earth Data Science"? Big data technologies are being widely practiced in Earth sciences and remote sensing communities to support EO data access, processing, and knowledge discovery. The data-intensive scientific discovery, named as the fourth paradigm, leads to the data science in the big data era. According to the definition by U.S. NIST (National Institute of Standards and Technology), the data science paradigm is the "extraction of actionable knowledge directly from data through a process of discovery, hypothesis, and hypothesis testing". The Earth Data Science is the art and science of applying data science paradigm to Earth observing data.
This workshop intends to identify significant trends and technological approaches in computing/storage/modelling infrastructures, data lifecycle management, and big data analytics, along with the development of relevant standards that enable Earth Data Science. The technical content will cover not only a variety of data models, computing methods, data storage solutions, and integrated modelling strategies, but also the increasing number of Earth data analytic methodologies borrowed from transfer learning, Mathematics, artificial neural networks and deep learning. The emphasis will be how those technologies change the way geospatial activities including geospatial data management, data processing, data analytics, and applications are being conducted.
The topics of the conference include but are not limited to:
- Geospatial big data management - curation, discovery and access,
- Big data analytics - methods, tools, and best practices,
- Web and Cloud-based processing of geospatial big data - standards, interoperability, geospatial workflows, and provenance,
- Hyper-dimensional geospatial data visualization - methods, tools, and applications,
- Social aspects of geospatial big data - collaboration, crowdsourcing, and volunteer geographic information,
- Directions and trends of geospatial big data science - AI, cognitive computing and beyond,
- Geospatial big data applications - agriculture, natural resources, disasters, and environment.
A call for papers is open until March 31 2018. For further information on the call for papers, visit http://geos.whu.edu.cn/bigdataconf/call-for-papers.html