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

DOI

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.

Identifier
DOI https://doi.org/10.11588/DATA/UHSQG0
Related Identifier IsCitedBy https://doi.org/10.1016/j.ophoto.2024.100059
Metadata Access https://heidata.uni-heidelberg.de/oai?verb=GetRecord&metadataPrefix=oai_datacite&identifier=doi:10.11588/DATA/UHSQG0
Provenance
Creator Vallejo Orti, Miguel; Anders, Katharina; Ajali, Oliubikum; Bubenzer, Olaf; Höfle, Bernhard
Publisher heiDATA
Contributor Vallejo Orti, Miguel
Publication Year 2024
Funding Reference The Kurt-Hiehle-Foundation ; Namibia University of Science and Technology - ILMI ; DLR TanDEM-X Science Team DEM_HYDR2024
Rights CC BY 4.0; info:eu-repo/semantics/openAccess; http://creativecommons.org/licenses/by/4.0
OpenAccess true
Contact Vallejo Orti, Miguel (3D Geospatial Data Processing Group, Institute of Geography, Heidelberg University, Germany)
Representation
Resource Type Dataset
Format application/zip; text/x-python; text/plain; text/tab-separated-values
Size 1962188; 24318; 2724; 32388; 7124; 4047002; 15800
Version 1.0
Discipline Earth and Environmental Science; Environmental Research; Geosciences; Natural Sciences