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Open source and web-based GeoAI tool for transparent forest fire prediction ; Open-Source- und webbasiertes GeoKI-Tool zur transparenten Waldbrandvorhersage

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Open source and web-based GeoAI tool for transparent forest fire prediction ; Open-Source- und webbasiertes GeoKI-Tool zur transparenten Waldbrandvorhersage
Συγγραφείς: Meier, Sebastian
Έτος έκδοσης: 2024
Θεματικοί όροι: article, ddc:520, Spatio-temporal analyses and models, predict forest fire susceptibility, prognosis, open geospatial data, Artificial Intelligence (AI), Geospatial Artificial Intelligence (GeoAI), transparency, case study, full-stack web-GIS, user interaction, map rendering, Google Earth Engine (GEE), Geospatial Data Abstraction Library (GDAL), Python , Maplibre GL JS, TiTiler, raum-zeitliche Analysen und Modelle, Waldbrandanfälligkeit, Prognose, Open Data, offene Geodaten, Künstliche Intelligenz, KI, Transparenz, Fallstudie, Benutzerinteraktion, Kartendarstellung, Open Source
Θέμα γεωγραφικό: Brandenburg, 13.01582 52.45905
Περιγραφή: Utilizing open geospatial data and AI, we are trying to predict forest fire susceptibility in Brandenburg. This case study showcases full-stack web-GIS, emphasizing user interaction and transparent AI through open-source tools like GEE, GDAL, Python, Maplibre, and TiTiler.
Τύπος εγγράφου: article in journal/newspaper
Περιγραφή αρχείου: 1 Online-Ressource (Seite 79, 36,77 kB) : 1 Textdatei (PDF)
Γλώσσα: English
ISBN: 978-1-887333-25-2
978-3-00-077982-4
1-887333-25-8
3-00-077982-5
Relation: FOSSGIS 2024, in Hamburg, 20.-23. März 2024; FOSSGIS 2024, in Hamburg, March 20-23, 2024; urn:nbn:de:0307-20240419-003-4; https://doi.org/10.5281/zenodo.10570705; https://zenodo.org/records/10570705; https://files.fossgis.de/Konferenz/2024/fossgis_tagungsband_2024_digital.pdf; https://www.fossgis-konferenz.de/2024/; https://doi.org/10.5446/s_1800; http://uri.gbv.de/document/gvk:ppn:1887333258; https://pretalx.com/fossgis2024/talk/TCVX3C/; https://doi.org/10.5446/67607
DOI: 10.48711/20240429-001
DOI: 10.5446/67607
Διαθεσιμότητα: https://doi.org/10.48711/20240429-001
https://nbn-resolving.org/urn:nbn:de:0307-20240429-002-3
https://kartdok.staatsbibliothek-berlin.de/receive/kartdok_mods_00001034
https://kartdok.staatsbibliothek-berlin.de/servlets/MCRZipServlet/kartdok_mods_00001034
https://pretalx.com/fossgis2024/talk/TCVX3C/
https://doi.org/10.5446/67607
Rights: public ; https://creativecommons.org/licenses/by/4.0/ ; info:eu-repo/semantics/openAccess
Αριθμός Καταχώρησης: edsbas.ECAD5385
Βάση Δεδομένων: BASE
FullText Text:
  Availability: 0
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  – Url: https://doi.org/10.48711/20240429-001#
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PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 949.707946777344
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  Data: Open source and web-based GeoAI tool for transparent forest fire prediction ; Open-Source- und webbasiertes GeoKI-Tool zur transparenten Waldbrandvorhersage
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  Data: <searchLink fieldCode="AR" term="%22Meier%2C+Sebastian%22">Meier, Sebastian</searchLink>
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  Data: <searchLink fieldCode="DE" term="%22article%22">article</searchLink><br /><searchLink fieldCode="DE" term="%22ddc%3A520%22">ddc:520</searchLink><br /><searchLink fieldCode="DE" term="%22Spatio-temporal+analyses+and+models%22">Spatio-temporal analyses and models</searchLink><br /><searchLink fieldCode="DE" term="%22predict+forest+fire+susceptibility%22">predict forest fire susceptibility</searchLink><br /><searchLink fieldCode="DE" term="%22prognosis%22">prognosis</searchLink><br /><searchLink fieldCode="DE" term="%22open+geospatial+data%22">open geospatial data</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+Intelligence+%28AI%29%22">Artificial Intelligence (AI)</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+Artificial+Intelligence+%28GeoAI%29%22">Geospatial Artificial Intelligence (GeoAI)</searchLink><br /><searchLink fieldCode="DE" term="%22transparency%22">transparency</searchLink><br /><searchLink fieldCode="DE" term="%22case+study%22">case study</searchLink><br /><searchLink fieldCode="DE" term="%22full-stack+web-GIS%22">full-stack web-GIS</searchLink><br /><searchLink fieldCode="DE" term="%22user+interaction%22">user interaction</searchLink><br /><searchLink fieldCode="DE" term="%22map+rendering%22">map rendering</searchLink><br /><searchLink fieldCode="DE" term="%22Google+Earth+Engine+%28GEE%29%22">Google Earth Engine (GEE)</searchLink><br /><searchLink fieldCode="DE" term="%22Geospatial+Data+Abstraction+Library+%28GDAL%29%22">Geospatial Data Abstraction Library (GDAL)</searchLink><br /><searchLink fieldCode="DE" term="%22Python+<computer+program+language>%22">Python <computer program language></searchLink><br /><searchLink fieldCode="DE" term="%22Maplibre+GL+JS%22">Maplibre GL JS</searchLink><br /><searchLink fieldCode="DE" term="%22TiTiler%22">TiTiler</searchLink><br /><searchLink fieldCode="DE" term="%22raum-zeitliche+Analysen+und+Modelle%22">raum-zeitliche Analysen und Modelle</searchLink><br /><searchLink fieldCode="DE" term="%22Waldbrandanfälligkeit%22">Waldbrandanfälligkeit</searchLink><br /><searchLink fieldCode="DE" term="%22Prognose%22">Prognose</searchLink><br /><searchLink fieldCode="DE" term="%22Open+Data%22">Open Data</searchLink><br /><searchLink fieldCode="DE" term="%22offene+Geodaten%22">offene Geodaten</searchLink><br /><searchLink fieldCode="DE" term="%22Künstliche+Intelligenz%22">Künstliche Intelligenz</searchLink><br /><searchLink fieldCode="DE" term="%22KI%22">KI</searchLink><br /><searchLink fieldCode="DE" term="%22Transparenz%22">Transparenz</searchLink><br /><searchLink fieldCode="DE" term="%22Fallstudie%22">Fallstudie</searchLink><br /><searchLink fieldCode="DE" term="%22Benutzerinteraktion%22">Benutzerinteraktion</searchLink><br /><searchLink fieldCode="DE" term="%22Kartendarstellung%22">Kartendarstellung</searchLink><br /><searchLink fieldCode="DE" term="%22Open+Source%22">Open Source</searchLink>
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  Data: Utilizing open geospatial data and AI, we are trying to predict forest fire susceptibility in Brandenburg. This case study showcases full-stack web-GIS, emphasizing user interaction and transparent AI through open-source tools like GEE, GDAL, Python, Maplibre, and TiTiler.
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  Data: FOSSGIS 2024, in Hamburg, 20.-23. März 2024; FOSSGIS 2024, in Hamburg, March 20-23, 2024; urn:nbn:de:0307-20240419-003-4; https://doi.org/10.5281/zenodo.10570705; https://zenodo.org/records/10570705; https://files.fossgis.de/Konferenz/2024/fossgis_tagungsband_2024_digital.pdf; https://www.fossgis-konferenz.de/2024/; https://doi.org/10.5446/s_1800; http://uri.gbv.de/document/gvk:ppn:1887333258; https://pretalx.com/fossgis2024/talk/TCVX3C/; https://doi.org/10.5446/67607
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        Value: 10.48711/20240429-001
    Languages:
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      – SubjectFull: Brandenburg
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      – SubjectFull: 13.01582 52.45905
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      – SubjectFull: Spatio-temporal analyses and models
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      – SubjectFull: predict forest fire susceptibility
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      – SubjectFull: prognosis
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      – TitleFull: Open source and web-based GeoAI tool for transparent forest fire prediction ; Open-Source- und webbasiertes GeoKI-Tool zur transparenten Waldbrandvorhersage
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              Y: 2024
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