RINX 2.0: A Containerized Climate Raster Information Extraction System on OpenShift Cloud Environment

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Publicado en:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences vol. X-G-2025 (2025), p. 391
Autor principal: Jain, Devika
Otros Autores: Blossom, Jeff, Hayes, Jack, Gibson, Heike, Rifas-Shimann, Sheryl, Gold, Diane R
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Copernicus GmbH
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Acceso en línea:Citation/Abstract
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100 1 |a Jain, Devika  |u Center for Geographic Analysis, Harvard University, Cambridge, MA, USA; Center for Geographic Analysis, Harvard University, Cambridge, MA, USA 
245 1 |a RINX 2.0: A Containerized Climate Raster Information Extraction System on OpenShift Cloud Environment 
260 |b Copernicus GmbH  |c 2025 
513 |a Journal Article 
520 3 |a RINX (Raster INformation eXtraction) 2.0 is an advanced solution for efficiently extracting climate data from large raster datasets in a cloud computing environment. Building upon the original RINX 1.0, which utilized high-performance computing clusters, RINX 2.0 leverages cloud technologies such as OpenShift and PostGIS to handle massive datasets and automate the extraction process. The system supports large-scale spatiotemporal raster extractions, processing over 158 million data points from the 15TB PRISM climate dataset. Here, we describe the architecture, methods, and tools used in RINX 2.0, including containerized environments, automated data pipelines, and integration with the New England Research Cloud. The system was deployed for the Environmental influences on Child Health Outcomes (ECHO) project, providing valuable insights into environmental health research. We present performance statistics, data management strategies, and the development of a user interface for real-time querying and visualization of results. 
653 |a Data management 
653 |a Datasets 
653 |a Environmental health 
653 |a Raster 
653 |a Environmental research 
653 |a Climatic data 
653 |a Real time 
653 |a Information retrieval 
653 |a Massive data points 
653 |a Cloud computing 
653 |a Data points 
653 |a Childrens health 
653 |a Software packages 
653 |a Humidity 
653 |a Remote sensing 
653 |a Photogrammetry 
653 |a Software services 
653 |a Data analysis 
653 |a Automation 
653 |a High performance computing 
653 |a Efficiency 
653 |a Case studies 
700 1 |a Blossom, Jeff  |u Center for Geographic Analysis, Harvard University, Cambridge, MA, USA; Center for Geographic Analysis, Harvard University, Cambridge, MA, USA 
700 1 |a Hayes, Jack  |u Center for Geographic Analysis, Harvard University, Cambridge, MA, USA; Center for Geographic Analysis, Harvard University, Cambridge, MA, USA 
700 1 |a Gibson, Heike  |u Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA 
700 1 |a Rifas-Shimann, Sheryl  |u Department of Population Medicine, Harvard Medical School, Boston, MA, USA; Department of Population Medicine, Harvard Medical School, Boston, MA, USA 
700 1 |a Gold, Diane R  |u Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA 
773 0 |t ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  |g vol. X-G-2025 (2025), p. 391 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3228874399/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3228874399/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch