Dr.-Ing. Matthias Häberle

Room:  240 (TUM Ottobrunn Campus)
E-mail: Matthias.Haeberle(at)tum.de


Curriculum Vitae

  • Since 05.2022, Postdoc at AI4EO Future Lab In Ottobrunn

  • 08.2017 – 05.2022, Research Associate, Munich Aerospace e.V. & DLR

  • 09.2013 - 09.2016, Master in Cognitive Science, Albert-Ludwigs-University Freiburg

  • 09.2009 - 07.2013, Bachelor in Communication and Software Technology, University of Applied Science Albstadt-Sigmaringen

Research Interests

  • Ethics in AI in general and focus on Earth Observation as well

  • Geo-spatial natural language processing using social media data and geo-referenced text

  • Fusion of social media data and remote sensing data using deep learning and natural language processing technologies


Häberle, M., Hoffmann, E. J., Zhu X.X., 2022. Can linguistic features extracted from geo-referenced tweets help building function classification in remote sensing?, ISPRS Journal of Photogrammetry and Remote Sensing, Volume 188, June 2022, Pages 255-268

Kruspe, A., Häberle, M., Hoffmann, E. J., Rode-Hasinger, S., Abdulahhad, K. Zhu, X.X., 2021. Changes in Twitter geolocations: Insights and suggestions for future usage. In Proceedings of the Seventh Workshop on Noisy User-generated Text (W-NUT 2021), pages 212–221, Online. Association for Computational Linguistics.

Kruspe, A., Häberle, M., Kuhn I., Zhu X.X., 2020. Cross-language sentiment analysis of European Twitter messages during the COVID-19 pandemic. In Proceedings of the 1st Workshop on NLP for COVID-19 at ACL 2020, Online. Association for Computational Linguistics.

Häberle M., Werner M., Zhu X.X., 2019. Building Type Classification from Social Media Texts via geo-spatial Text Mining, accepted at the 2019 IEEE International Geo-Science and Remote Sensing Symposium (IGARSS), Yokohama, Japan. August 2019.

Häberle M., Werner M., Zhu X.X., 2018. Geo-Spatial Text-Mining from Twitter—A Feature Space Analysis with a view towards Building Classification in Urban Regions, European Journal of Remote Sensing.