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Integrating VGI contributions for gully mapping using Kalman filter and machine learning

The codes and datsets included are related to experiments and results conducted to integrate different lines digitized by volunteers using Kalman filter with changing amount of input lines. Three approaches are included: i) Kalman filtering integration to investigate the role of basemaps and a numbe...

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Bibliographische Detailangaben
Hauptverfasser: Vallejo Orti, Miguel (VerfasserIn) , Anders, Katharina (VerfasserIn) , Ajali, Oliubikum (VerfasserIn) , Bubenzer, Olaf (VerfasserIn) , Höfle, Bernhard (VerfasserIn)
Dokumenttyp: Datenbank Forschungsdaten
Sprache:Englisch
Veröffentlicht: Heidelberg Universität 2024-02-19
DOI:10.11588/data/UHSQG0
Schlagworte:
Forschungsdaten
Datenbank
Online-Zugang:Resolving-System, kostenfrei, Volltext: https://doi.org/10.11588/data/UHSQG0
Verlag, kostenfrei, Volltext: https://heidata.uni-heidelberg.de/dataset.xhtml?persistentId=doi:10.11588/data/UHSQG0
Volltext
Verfasserangaben:Miguel Vallejo Orti, Katharina Anders, Oliubikum Ajali, Olaf Bubenzer, Bernhard Höfle
Beschreibung
Zusammenfassung:The codes and datsets included are related to experiments and results conducted to integrate different lines digitized by volunteers using Kalman filter with changing amount of input lines. Three approaches are included: i) Kalman filtering integration to investigate the role of basemaps and a number of contributions, ii) Kalman filtering coupled with a self-learning stratergy and, iii) a cross-training strategy. (2024-01-03)
Beschreibung:Gefördert durch: The Kurt-Hiehle-Foundation, Namibia University of Science and Technology - ILMI, DLR TanDEM-X Science Team: DEM_HYDR2024
Gesehen am 13.05.2024
Beschreibung:Online Resource
DOI:10.11588/data/UHSQG0

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