@Book{1839123516,
author="Hagenauer, Julian Christian",
title="Challenges and prospects of spatial machine learning",
year="2022",
address="Rostock",
abstract="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>",
school="Universit{\"a}t Rostock",
note="vorgelegt von Julian Christian Hagenauer",
note="Enth{\"a}lt Zeitschriftenartikel",
note="GutachterInnen: Philip Marzahn (Universit{\"a}t Rostock, Agrar- und Umweltwissenschaftliche Fakult{\"a}t) ; Nguyen Xuan Thinh (Technische Universit{\"a}t Dortmund, Fakult{\"a}t Raumplanung) ; Johannes Scholz (Technische Universit{\"a}t Graz, Geod{\"a}tisches Institut)",
note="Habilitationsschrift Universit{\"a}t Rostock 2023 Kumulative Habilitationsschrift",
doi="10.18453/rosdok_id00004228",
url="http://purl.uni-rostock.de/rosdok/id00004228",
url="https://nbn-resolving.org/urn:nbn:de:gbv:28-rosdok_id00004228-3",
url="https://d-nb.info/129354065X/34",
url="https://doi.org/10.18453/rosdok_id00004228",
language="English"
}