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000911816 037__ $$aFZJ-2022-05064
000911816 041__ $$aEnglish
000911816 1001_ $$0P:(DE-Juel1)186637$$aSelke, Niklas$$b0$$eCorresponding author$$ufzj
000911816 1112_ $$aLiving Planet Symposium 2022$$cBonn$$d2022-05-23 - 2022-05-27$$gLPS2022$$wGermany
000911816 245__ $$aGeodata enrichment for air quality
000911816 260__ $$c2022
000911816 3367_ $$033$$2EndNote$$aConference Paper
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000911816 520__ $$aDuring 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
000911816 536__ $$0G:(DE-HGF)POF4-5111$$a5111 - Domain-Specific Simulation & Data Life Cycle Labs (SDLs) and Research Groups (POF4-511)$$cPOF4-511$$fPOF IV$$x0
000911816 536__ $$0G:(EU-Grant)787576$$aIntelliAQ - Artificial Intelligence for Air Quality (787576)$$c787576$$fERC-2017-ADG$$x1
000911816 536__ $$0G:(DE-Juel-1)ESDE$$aEarth System Data Exploration (ESDE)$$cESDE$$x2
000911816 7001_ $$0P:(DE-Juel1)177004$$aLeufen, Lukas Hubert$$b1$$ufzj
000911816 7001_ $$0P:(DE-Juel1)166264$$aMozaffari, Amirpasha$$b2$$ufzj
000911816 7001_ $$0P:(DE-Juel1)16212$$aSchröder, Sabine$$b3$$ufzj
000911816 7001_ $$0P:(DE-Juel1)6952$$aSchultz, Martin$$b4$$ufzj
000911816 8564_ $$uhttps://juser.fz-juelich.de/record/911816/files/LPS22_Poster_Niklas_Selke.pdf$$yOpenAccess
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000911816 9141_ $$y2022
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000911816 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)16212$$aForschungszentrum Jülich$$b3$$kFZJ
000911816 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)6952$$aForschungszentrum Jülich$$b4$$kFZJ
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000911816 9201_ $$0I:(DE-Juel1)JSC-20090406$$kJSC$$lJülich Supercomputing Center$$x0
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