Cloud Optimized Raster Encoding (CORE): A Web-Native Streamable Format for Large Environmental Time Series
| Autor: | Lucia de Espona, Christian Ginzler, Rebecca Kurup Buchholz, Loïc Pellissier, Niklaus E. Zimmermann, Gian-Kasper Plattner, Marius Rüetschi, Dirk Nikolaus Karger, Martin Hägeli, Dominik Haas-Artho, David Hanimann, Ionuț Iosifescu Enescu |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Geospatial analysis
raster time series Computer science Cloud computing data cube computer.software_genre Environmental data hypercube Data cube Raster data open software COG EnviDat CORE environmental open data QE1-996.5 Database business.industry Geology web-EGIS computer.file_format Visualization GeoTIFF very large geodata Raster graphics business computer |
| Zdroj: | Geomatics Volume 1 Issue 3 Pages 21-382 Geomatics, Vol 1, Iss 21, Pp 369-382 (2021) |
| ISSN: | 2673-7418 |
| DOI: | 10.3390/geomatics1030021 |
| Popis: | The Environmental Data Portal EnviDat aims to fuse data publication repository functionalities with next-generation web-based environmental geospatial information systems (web-EGIS) and Earth Observation (EO) data cube functionalities. User requirements related to mapping and visualization represent a major challenge for current environmental data portals. The new Cloud Optimized Raster Encoding (CORE) format enables an efficient storage and management of gridded data by applying video encoding algorithms. Inspired by the cloud optimized GeoTIFF (COG) format, the design of CORE is based on the same principles that enable efficient workflows on the cloud, addressing web-EGIS visualization challenges for large environmental time series in geosciences. CORE is a web-native streamable format that can compactly contain raster imagery as a data hypercube. It enables simultaneous exchange, preservation, and fast visualization of time series raster data in environmental repositories. The CORE format specifications are open source and can be used by other platforms to manage and visualize large environmental time series. |
| Databáze: | OpenAIRE |
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