Devis Tuia
EPFL ENAC IIE ECEO
EPFL Valais Wallis
Rue de l'Industrie 17
Case postale 440
CH-1951 Sion
+41 21 693 00 11
+41 21 693 82 83
Office: R35 1 02
EPFL
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ENAC
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IIE
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ECEO
Web site: Web site: https://www.epfl.ch/labs/eceo/
Fields of expertise
- Earth Observation, remote sensing: from drones to satellites
- Machine learning, deep learning
- Image processing
- 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
- 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, where I started the 'multimodal remote sensing' group. In 2017, I joined Wageningen University (NL), where I was professor of the GeoInformation Science and Remote Sensing Laboratory. Since 2020, I joined EPFL Valais, to start the ECEO lab, working at the interface between Earth observation, machine learning and environmental sciences.
Publications
Selected publications
| Lobry, S., Marcos, D., Murray, J., Tuia, D. IEEE Transactions on Geoscience and Remote Sensing |
RSVQA: visual question answering for remote sensing data |
| Kellenberger, B., Marcos, D., Tuia, D. Remote Sensing of Environment |
Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning |
| Zhu, X.X., Tuia, D., Mou, L., Xia, G., Zhang, L., Xu, F. Fraundorfer, F. IEEE Geoscience and Remote Sensing Magazine |
Deep learning in remote sensing: A comprehensive review and list of resources |
Teaching & PhD
Teaching
Environmental Sciences and Engineering