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/
EPFL > VPA-AVP-CP > SCITAS > SCITAS-CD
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
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
PhD Students
Béchaz Manon Cécile Nicole, Gabeff Valentin Alexandre Guy, May Giacomo Günter, Mi Li, Nguyen Thiên-Anh Claris, Pisl Jan, Porta Hugo Laurent Pascal, Sauder Jonathan Paul, Tartini-Chappuis Christel Marie, Van Tiel Nina Marion Aurélia, Xu Chang, Zbinden Robin Adrien, Zermatten Valérie,Past EPFL PhD Students
Parkan Matthew Josef , Produit Timothée ,Courses
Frontiers of Deep Learning for Engineers
The seminar aims at discussing recent research papers in the field of deep learning, implementing the transferability/adaptability of the proposed approaches to applications in the field of research of the Ph.D. student.
Fundamentals of geomatics
Fundamental of geomatics for civil and environmental engineers. Introduction to acquisition, management and visualization of geodata. Learning and doing practical experiments: geodata acquisition and land imaging.
Sensing and spatial modeling for earth observation
Students get acquainted with the process of mapping from images (orthophoto and DEM), as well as with methods for monitoring the Earth surface using remotely sensed data. Methods will span from machine learning to geostatistics and model the spatiotemporal variability of processes.
Image processing for Earth observation
This course covers optical remote sensing from satellites and airborne platforms. The different systems are presented. The students will acquire skills in image processing and machine/deep learning to extract end-products from the images such as land cover or risk maps.