D
Devis Tuia
Researcher at École Polytechnique Fédérale de Lausanne
Publications - 314
Citations - 16928
Devis Tuia is an academic researcher from École Polytechnique Fédérale de Lausanne. The author has contributed to research in topics: Contextual image classification & Support vector machine. The author has an hindex of 54, co-authored 287 publications receiving 12442 citations. Previous affiliations of Devis Tuia include University of Toronto & University of Valencia.
Papers
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Journal ArticleDOI
Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: The challenges of using deep learning for remote-sensing data analysis are analyzed, recent advances are reviewed, and resources are provided that hope will make deep learning in remote sensing seem ridiculously simple.
Journal ArticleDOI
Optimal Transport for Domain Adaptation
TL;DR: A regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains, that consistently outperforms state of the art approaches and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.
Proceedings ArticleDOI
DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images
Ilke Demir,Krzysztof Koperski,David Lindenbaum,Guan Pang,Jing Huang,Saikat Basu,Forest Hughes,Devis Tuia,Ramesh Raska +8 more
TL;DR: The DeepGlobe 2018 Satellite Image Understanding Challenge is presented, which includes three public competitions for segmentation, detection, and classification tasks on satellite images, and characteristics of each dataset are analyzed, and evaluation criteria for each task are defined.
Journal ArticleDOI
Deep learning in remote sensing: a review
Xiao Xiang Zhu,Devis Tuia,Lichao Mou,Gui-Song Xia,Liangpei Zhang,Feng Xu,Friedrich Fraundorfer +6 more
TL;DR: In this article, the authors analyze the challenges of using deep learning for remote sensing data analysis, review the recent advances, and provide resources to make deep learning in remote sensing ridiculously simple to start with.
Journal ArticleDOI
Advances in Hyperspectral Image Classification: Earth Monitoring with Statistical Learning Methods
TL;DR: In this paper, the authors focus on the challenging problem of hyperspectral image classification, which has recently gained in popularity and attracted the interest of other scientific disciplines such as machine learning, image processing, and computer vision.