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

• Verfasst von: Vallejo Orti, Miguel [VerfasserIn]
• Verfasst von: Anders, Katharina [VerfasserIn]
• Verfasst von: Ajali, Oliubikum [VerfasserIn]
• Verfasst von: Bubenzer, Olaf [VerfasserIn]
• Verfasst von: Höfle, Bernhard [VerfasserIn]
• Titel: Integrating VGI contributions for gully mapping using Kalman filter and machine learning
• Verf.angabe: Miguel Vallejo Orti, Katharina Anders, Oliubikum Ajali, Olaf Bubenzer, Bernhard Höfle
• Verlagsort: Heidelberg
• Verlag: Universität
• E-Jahr: 2024
• Jahr: 2024-02-19
• Umfang: 1 Online-Ressource (7 Files)
• Fussnoten: 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
• DOI: doi:10.11588/data/UHSQG0
• Datenträger: Online-Ressource
• Dokumenttyp: Forschungsdaten
• Dokumenttyp: Datenbank
• Sprache: eng
• Bibliogr. Hinweis: Forschungsdaten zu: Vallejo Orti, Miguel, 1983 - : Integrating multi-user digitising actions for mapping gully outlines using a combined approach of Kalman filtering and machine learning
• Sonstige Nr.: Grant number: DLR, DEM_HYDR2024
• Sach-SW: Earth and Environmental Sciences
• K10plus-PPN: 1888406216
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

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)