Devis Tuia
EPFL Valais Wallis
EPFL ENAC IIE ECEO
Route des Ronquos 86
1951 Sion
Web site: Web site: https://www.epfl.ch/labs/eceo/
Web site: Web site: https://go.epfl.ch/phd-edce
+41 21 693 00 11
EPFL
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ENAC
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ENAC-SSIE
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SSIE-GE
Web site: Web site: https://ssie.epfl.ch/
Fields of expertise
- Machine learning, deep learning
- Image processing
Current work
- Making remote sensing accessible to everyone! Developing algorithms for human machine interaction- Open the black box: interpretable deep learning and uncertainties in environmental modeling
- Digital wildlife conservation: using imaging to automatize censuses and conservation efforts
Biography
I come from Ticino and studied in Lausanne, between UNIL and EPFL. After my PhD at UNIL in remote sensing, I was postdoc in Valencia (Spain), Boulder (CO) and EPFL, working on model adaptation and prior knowledge integration in machine learning. In 2014 I became Research Assistant Professor at University of Zurich, then Associate and Full professor at Wageningen University in the Netherlands. Since 2020 I lead the ECEO laboratory of EPFL, where we study Earth from above with machine learning, computer vision and remote sensing.Publications
Selected publications
| D. Tuia, B. Kellenberger, S. Beery, B. Costelloe, S. Zuffi, B. Risse, A. Mathis, M. W. Mathis et al. Nature Communications |
Perspectives in machine learning for wildlife conservation |
| G. Camps-Valls, D. Tuia, X. X. Zhu, M. Reichstein Wiley |
Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences. |
| D. Tuia, R. Roscher, J. D. Wegner, N. Jacobs, X. X. Zhu, G. Camps-Valls IEEE Geoscience and Remote Sensing Magazine |
Towards a collective agenda on AI for earth science data analysis |
| B, Kellenberger, D., Marcos, D., Tuia Remote Sensing of Environment |
Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning |
| S. Lobry, D., Marcos, J., Murray, D., Tuia IEEE Transactions on Geoscience and Remote Sensing |
RSVQA: visual question answering for remote sensing data |
Teaching & PhD
Teaching
Environmental Sciences and Engineering