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@INPROCEEDINGS{Selke:911816,
      author       = {Selke, Niklas and Leufen, Lukas Hubert and Mozaffari,
                      Amirpasha and Schröder, Sabine and Schultz, Martin},
      title        = {{G}eodata enrichment for air quality},
      reportid     = {FZJ-2022-05064},
      year         = {2022},
      abstract     = {During the Tropospheric Ozone Assessment Report (TOAR) [1]
                      we built an air quality database that contains time series
                      of measured ozone, ozone precursors, and meteorological data
                      from surface observation stations. One aspect of the TOAR
                      database that substantially contributed to its adoption by
                      the research community, is the augmentation of provider
                      metadata for these stations with globally consistent
                      information derived from multiple Earth Observation data
                      products. This adds additional context to the description of
                      measurement locations and thereby enriches the analysis
                      possibilities. For this we developed a workflow called
                      Geolocation Service that we want to present here.Our
                      Geolocation Service exposes REST APIs to the user where they
                      can specify an area of interest in the form of latitude,
                      longitude, and possibly radius as well as a specific time
                      where we have data with a time resolution. With the radius
                      parameter it is possible to extract points (no radius) or
                      areas. Different REST API endpoints provide different
                      services, like for example topographic information and
                      nighttime lights. The advantage of REST APIs is that they
                      not only make human interaction possible but also machine to
                      machine communication.After retrieving the requested data
                      from a performant geodata service (in our case a Rasdaman
                      service), the service can run different analyses which the
                      user can specify and will return any results in a
                      standardized way, namely Geo-JSON. The user can choose
                      between a range of aggregation methods (mean, min, max,
                      etc.) or can choose to return the closest value to the given
                      coordinates. The aggregation method can be specified
                      directly in the REST APIs which makes it very flexible for
                      the user.Since this workflow consists of modular components,
                      it is easy to exchange or expand some of its parts. We are
                      interested in expanding the available datasets to include
                      the Copernicus Sentinels (i. e. retrieving data from the
                      Copernicus Open Access Hub instead of from our Rasdaman
                      service) to run the existing analyses on those datasets
                      while still providing the user with the same interface and
                      responses as they already know.To go even further, it would
                      also be possible to include landcover detection, flood
                      mapping, and other spatial analyses via the same geodata
                      workflow.[1] https://igacproject.org/activities/TOAR},
      month         = {May},
      date          = {2022-05-23},
      organization  = {Living Planet Symposium 2022, Bonn
                       (Germany), 23 May 2022 - 27 May 2022},
      subtyp        = {After Call},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / IntelliAQ -
                      Artificial Intelligence for Air Quality (787576) / Earth
                      System Data Exploration (ESDE)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)787576 /
                      G:(DE-Juel-1)ESDE},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/911816},
}