%0 Book
%T Challenges and prospects of spatial machine learning
%A Hagenauer, Julian Christian
%D 2022
%C Rostock
%C Universität Rostock
%G English
%F 1839123516
%O vorgelegt von Julian Christian Hagenauer
%O Enthält Zeitschriftenartikel
%O GutachterInnen: Philip Marzahn (Universität Rostock, Agrar- und Umweltwissenschaftliche Fakultät) ; Nguyen Xuan Thinh (Technische Universität Dortmund, Fakultät Raumplanung) ; Johannes Scholz (Technische Universität Graz, Geodätisches Institut)
%O Habilitationsschrift Universität Rostock 2023 Kumulative Habilitationsschrift
%X The main objective of this thesis is to improve the usefulness of spatial machine learning for the spatial sciences and to allow its unused potential to be exploited. To achieve this objective, this thesis addresses several important but distinct challenges which spatial machine learning is facing. These are the modeling of spatial autocorrelation and spatial heterogeneity, the selection of an appropriate model for a given spatial problem, and the understanding of complex spatial machine learning models.<eng>
%L 004
%9 theses
%9 Text
%9 Hochschulschrift
%R 10.18453/rosdok_id00004228
%U http://purl.uni-rostock.de/rosdok/id00004228
%U https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00004228-3
%U https://d-nb.info/129354065X/34
%U https://doi.org/10.18453/rosdok_id00004228