Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
9,867
result(s) for
"Earth sciences Data processing."
Sort by:
Deep learning for the earth sciences : a comprehensive approach to remote sensing, climate science and geosciences
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception.
eBook
A roadmap for a dedicated Earth Science Grid platform
Due to its intensive data processing and highly distributed organization, the multidisciplinary Earth Science applications community is uniquely positioned for the uptake and exploitation of Grid technologies. Currently Enabling Grids for E-sciencE, and other large Grid infrastructures are already deployed and capable of operational services. So far however, the adoption and exploitation of Grid technology throughout the Earth Science community has been slower than expected. The Dissemination and Exploitation of GRids in Earth sciencE project, proposed by the European Commission to assist and accelerate this process in a number of different ways, had between its main goals the creation of a roadmap towards Earth Science Grid platform. This paper presents the resulting roadmap.
Journal Article
Geographical information systems : trends and technologies
\"Preface Geographical Information Systems (GIS) since its inception in the late 1960s have seen an increasing rate of theoretical, technological and organizational development. Developments in each decade of the last 50 years highlight particular innovations in this fi eld. The mid 1960s witnessed the initial development of GIS in combining spatially referenced data, spatial data models and data visualization. The early 1970s witnessed the ability of computer mapping in automatic map drafting and using data format. In the 1980s, computer mapping capabilities have been merged with traditional database management systems capabilities to generate spatial database management systems. Accordingly, the ability to select, sort, extract, classify and display geographic data on the basis of complex topological and statistical criteria was available to users. The 1990s saw map analysis and modeling advances in GIS, and these systems became real management information tools as computing power increased. During this decade, the Open GIS Consortium, aimed at developing publicly available geoprocessing specifi cations, was founded. Since 2000, with the advent of Web 2.0, mobile, and wireless technologies, GIS have been moving towards an era in which the power of such systems is continuously increasing in multiple facets consisting of computing, visualizing, mining, reasoning data. The latest changes in technologies and trends have brought new challenges and opportunities in GIS domain. Specifi cally, mobile and internet devices, Cloud computing, NoSQL databases, Semantic Web, Web services offer new ways of accessing, analyzing, and elaborating geospatial information in both real-world and virtual spaces\"-- Provided by publisher.
Book
An Overview of Platforms for Big Earth Observation Data Management and Analysis
In recent years, Earth observation (EO) satellites have generated big amounts of geospatial data that are freely available for society and researchers. This scenario brings challenges for traditional spatial data infrastructures (SDI) to properly store, process, disseminate and analyze these big data sets. To meet these demands, novel technologies have been proposed and developed, based on cloud computing and distributed systems, such as array database systems, MapReduce systems and web services to access and process big Earth observation data. Currently, these technologies have been integrated into cutting edge platforms in order to support a new generation of SDI for big Earth observation data. This paper presents an overview of seven platforms for big Earth observation data management and analysis—Google Earth Engine (GEE), Sentinel Hub, Open Data Cube (ODC), System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL), openEO, JEODPP, and pipsCloud. We also provide a comparison of these platforms according to criteria that represent capabilities of the EO community interest.
Journal Article