Journal cover Journal topic
Advances in Cartography and GIScience of the ICA
Journal topic
Volume 2
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 2, 5, 2019
https://doi.org/10.5194/ica-adv-2-5-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
Adv. Cartogr. GIScience Int. Cartogr. Assoc., 2, 5, 2019
https://doi.org/10.5194/ica-adv-2-5-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  06 Nov 2019

06 Nov 2019

Towards Urban Environment Familiarity Prediction

Lukas Gokl1, Marvin Mc Cutchan1, Bartosz Mazurkiewicz1, Paolo Fogliaroni1,2, and Ioannis Giannopoulos1 Lukas Gokl et al.
  • 1Research Group Geoinformation, Vienna University of Technology, Austria
  • 2ESRI R&D Center Vienna, Austria

Keywords: environment familiarity, machine learning, virtual environment

Abstract. Location Based Services (LBS) are definitely very helpful for people that interact within an unfamiliar environment, but also for those that already possess a certain level of familiarity with it. In order to avoid overwhelming familiar users with unnecessary information, the level of details offered by the LBS shall be adapted to the level of familiarity with the environment: providing more details to unfamiliar users and a lighter amount of information (that would be superfluous, if not even misleading) to the users that are more familiar with the current environment. Currently, the information exchange between the service and its users is not taking into account familiarity. Within this work, we investigate the potential of machine learning for a binary classification of environment familiarity (i.e., familiar vs unfamiliar) with the surrounding environment. For this purpose, a 3D virtual environment based on a part of Vienna, Austria was designed using datasets from the municipal government. During a navigation experiment with 22 participants we collected ground truth data in order to train four machine learning algorithms. The captured data included motion and orientation of the users as well as visual interaction with the surrounding buildings during navigation. This work demonstrates the potential of machine learning for predicting the state of familiarity as an enabling step for the implementation of LBS better tailored to the user.

Publications Copernicus
Download
Citation