Julian Hagenauer

According to our database, Julian Hagenauer authored at least 15 papers between 2012 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of five.

2023

Letter to the Editor in reference to the article entitled '4D-GWR: geographically, altitudinal, and temporally weighted regression'.

Marco Helbich, Julian Hagenauer

Neural Comput. Appl., March, 2023

2022

A geographically weighted artificial neural network.

Julian Hagenauer, Marco Helbich

Int. J. Geogr. Inf. Sci., 2022

2019

Assessing the performance of 38 machine learning models: the case of land consumption rates in Bavaria, Germany.

Julian Hagenauer, Hichem Omrani, Marco Helbich

Int. J. Geogr. Inf. Sci., 2019

Big data in Geohazard; pattern mining and large scale analysis of landslides in Iran.

Hossein Shafizadeh-Moghadam, Masoud Minaei, Himan Shahabi, Julian Hagenauer

Earth Sci. Informatics, 2019

2018

Local modelling of land consumption in Germany with RegioClust.

Julian Hagenauer, Marco Helbich

Int. J. Appl. Earth Obs. Geoinformation, 2018

2017

A comparative study of machine learning classifiers for modeling travel mode choice.

Julian Hagenauer, Marco Helbich

Expert Syst. Appl., 2017

Data on Healthy Food Accessibility in Amsterdam, The Netherlands.

Marco Helbich, Julian Hagenauer

Data, 2017

2016

SPAWNN: A Toolkit for <i>SP</i>atial <i>A</i>nalysis <i>W</i>ith Self-Organizing <i>N</i>eural <i>N</i>etworks.

Julian Hagenauer, Marco Helbich

Trans. GIS, 2016

Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks.

Julian Hagenauer

J. Geogr. Syst., 2016

2015

Performance analysis of radial basis function networks and multi-layer perceptron networks in modeling urban change: a case study.

Hossein Shafizadeh-Moghadam, Julian Hagenauer, Manuchehr Farajzadeh, Marco Helbich

Int. J. Geogr. Inf. Sci., 2015

2013

Artificial neural networks: methods and applications in spatial data mining.

Julian Hagenauer

PhD thesis, 2013

Hierarchical self-organizing maps for clustering spatiotemporal data.

Julian Hagenauer, Marco Helbich

Int. J. Geogr. Inf. Sci., 2013

Contextual neural gas for spatial clustering and analysis.

Julian Hagenauer, Marco Helbich

Int. J. Geogr. Inf. Sci., 2013

Toward mapping land-use patterns from volunteered geographic information.

Jamal Jokar Arsanjani, Marco Helbich, Mohamed Bakillah, Julian Hagenauer, Alexander Zipf

Int. J. Geogr. Inf. Sci., 2013

2012

Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks.

Julian Hagenauer, Marco Helbich

Int. J. Geogr. Inf. Sci., 2012