| Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning B Kellenberger, D Marcos, D Tuia Remote sensing of environment 216, 139-153, 2018 | 343 | 2018 |
| Rotation equivariant vector field networks D Marcos, M Volpi, N Komodakis, D Tuia International Conference on Computer Vision (ICCV), 2017 | 313 | 2017 |
| Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models D Marcos, M Volpi, B Kellenberger, D Tuia ISPRS journal of photogrammetry and remote sensing 145, 96-107, 2018 | 274 | 2018 |
| RSVQA: Visual question answering for remote sensing data S Lobry, D Marcos, J Murray, D Tuia IEEE Transactions on Geoscience and Remote Sensing 58 (12), 8555-8566, 2020 | 225 | 2020 |
| Learning deep structured active contours end-to-end D Marcos, D Tuia, B Kellenberger, L Zhang, M Bai, R Liao, R Urtasun Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 205 | 2018 |
| Learning rotation invariant convolutional filters for texture classification D Marcos, M Volpi, D Tuia 2016 23rd International Conference on Pattern Recognition (ICPR), 2012-2017, 2016 | 196 | 2016 |
| Half a percent of labels is enough: Efficient animal detection in UAV imagery using deep CNNs and active learning B Kellenberger, D Marcos, S Lobry, D Tuia IEEE Transactions on Geoscience and Remote Sensing 57 (12), 9524-9533, 2019 | 138 | 2019 |
| A deep learning framework for matching of SAR and optical imagery LH Hughes, D Marcos, S Lobry, D Tuia, M Schmitt ISPRS Journal of Photogrammetry and Remote Sensing 169, 166-179, 2020 | 121 | 2020 |
| Semantic segmentation of remote sensing images with sparse annotations Y Hua, D Marcos, L Mou, XX Zhu, D Tuia IEEE Geoscience and Remote Sensing Letters 19, 1-5, 2021 | 116 | 2021 |
| Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series RN Masolele, V De Sy, M Herold, D Marcos, J Verbesselt, F Gieseke, ... Remote Sensing of Environment 264, 112600, 2021 | 91 | 2021 |
| DeSpeckNet: Generalizing deep learning-based SAR image despeckling AG Mullissa, D Marcos, D Tuia, M Herold, J Reiche IEEE Transactions on Geoscience and Remote Sensing 60, 1-15, 2020 | 79 | 2020 |
| Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization D Tuia, D Marcos, G Camps-Valls ISPRS Journal of Photogrammetry and Remote Sensing 120, 1-12, 2016 | 70 | 2016 |
| Interpretability of deep learning models for crop yield forecasting D Paudel, A De Wit, H Boogaard, D Marcos, S Osinga, IN Athanasiadis Computers and Electronics in Agriculture 206, 107663, 2023 | 66 | 2023 |
| Self-supervised pre-training enhances change detection in Sentinel-2 imagery M Leenstra, D Marcos, F Bovolo, D Tuia Pattern Recognition. ICPR International Workshops and Challenges: Virtual …, 2021 | 65 | 2021 |
| Scale equivariance in CNNs with vector fields D Marcos, B Kellenberger, S Lobry, D Tuia ICML Workshops, 2018 | 64 | 2018 |
| Social media and deep learning capture the aesthetic quality of the landscape I Havinga, D Marcos, PW Bogaart, L Hein, D Tuia Scientific reports 11 (1), 20000, 2021 | 42 | 2021 |
| Geospatial Correspondences for Multimodal Registration D Marcos, R Hamid, D Tuia Computer Vision and Pattern Recognition (CVPR), 2016 | 40 | 2016 |
| Contextual Semantic Interpretability D Marcos, R Fong, S Lobry, R Flamary, N Courty, D Tuia ACCV 2020, 2020 | 36 | 2020 |
| When a few clicks make all the difference: Improving weakly-supervised wildlife detection in UAV images B Kellenberger, D Marcos, D Tuia Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 36 | 2019 |
| Semantically Interpretable Activation Maps: what-where-how explanations within CNNs D Marcos, S Lobry, D Tuia 2019 ICCV Workshop on Interpreting and Explaining Visual Artificial …, 2019 | 33 | 2019 |
Devis TuiaEcole Polytechnique Fédérale de Lausanne (EPFL)確認したメール アドレス: epfl.ch