EGU Presentation

This work was partly presented at the EGU 2018 in the session Learning from spatial data: unveiling the geo-environment through quantitative approaches.

Python Codes

Here, you will find some Python codes. A step-by-step implementation using two Python libraries: TenserFlow and Keras with the TensorFlow back-end.


The benchmark datasets used in this work can be easily downloaded to a local workstation. They are freely available from open-source repositories.

Meet the team

Person 1

Mohamed Laib

Person 2

Jean Golay

Person 3

Fabian Guignard

Person 4

Mikhail Kanevski

Work place

Pic 02


Swiss Geocomputing Centre
Quartier UNIL-Mouline, Building Géopolis
1015 Lausanne, Switzerland.


A special thank to Ludovic Räss
for his assistance and input regarding
the efficient use of GPU clusters .

Pic 02