February 18, 2019 Conference paper Open Access

Neteler, Markus; Gebbert, Sören; Tawalika, Carmen; Bettge, Anika; Benelcadi, Hajar; Löw, Fabian; Adams, Till; Paulsen, Hinrich

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{
  "publisher": "P. Soille, S. Loekken, and S. Albani (Eds.)", 
  "DOI": "10.5281/zenodo.2631917", 
  "ISBN": "978-92-76-00034-1", 
  "container_title": "Proc. of the 2019 conference on Big Data from Space (BiDS'2019)", 
  "title": "Actinia: cloud based geoprocessing", 
  "issued": {
    "date-parts": [
      [
        2019, 
        2, 
        18
      ]
    ]
  }, 
  "abstract": "<p>Whether participatory urban planning, digital agriculture or near real-time monitoring of flooded plains &ndash; the demand for processing large quantities of Earth Observation (EO) and geodata is constantly increasing. In addition to the amount of data to be processed, the lack of compatibility between different data systems has often been an obstacle.<br>\nThe cloud based geoprocessing platform actinia is able to ingest and analyse large volumes of data already present in the cloud. Through actinia&rsquo;s REST API, following the paradigm of computing next to the data, users can now process and analyse EO- and geodata. Due to the scalability of cloud platforms, insights and tailor made information are delivered in near real-time. Furthermore, methods and algorithms can be easily integrated into own business processes.</p>\n\n<p>Actinia provides an open source REST API for scalable, distributed, and high performance processing of geographical data that mainly uses GRASS GIS for computational tasks.</p>", 
  "author": [
    {
      "family": "Neteler, Markus"
    }, 
    {
      "family": "Gebbert, S\u00f6ren"
    }, 
    {
      "family": "Tawalika, Carmen"
    }, 
    {
      "family": "Bettge, Anika"
    }, 
    {
      "family": "Benelcadi, Hajar"
    }, 
    {
      "family": "L\u00f6w, Fabian"
    }, 
    {
      "family": "Adams, Till"
    }, 
    {
      "family": "Paulsen, Hinrich"
    }
  ], 
  "id": "2631917", 
  "event-place": "Munich (Germany)", 
  "version": "1", 
  "publisher_place": "EUR 29660 EN, Publications Office of the European Union 5, Luxembourg", 
  "type": "paper-conference", 
  "event": "2019 Big Data from Space (BiDS'19) (BiDS)", 
  "page": "41-44"
}