Multisource; Multimodal; Very-high resolution; Data fusion; Machine learning; Uncertainty estimation; Hyperspectral imaging; Remote sensing; Crowdsourcing
Vargas Muñoz John E., Tuia Devis, Falcão Alexandre X. (2020), Deploying machine learning to assist digital humanitarians: making image annotation in OpenStreetMap more efficient, in International Journal of Geographical Information Science
Srivastava Shivangi, Vargas-Muñoz John E., Tuia Devis (2019), Understanding urban landuse from the above and ground perspectives: A deep learning, multimodal solution, in Remote Sensing of Environment
, 228, 129-143.
Marcos Diego, Marcos Diego, Volpi Michele, Kellenberger Benjamin, Tuia Devis, Tuia Devis (2018), Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models, in ISPRS Journal of Photogrammetry and Remote Sensing
, 145, 96-107.
Volpi Michele, Tuia Devis (2018), Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images, in ISPRS Journal of Photogrammetry and Remote Sensing
, 144, 48-60.
Kellenberger Benjamin, Marcos Diego, Tuia Devis (2018), Detecting mammals in UAV images: Best practices to address a substantially imbalanced dataset with deep learning, in Remote Sensing of Environment
, 216, 139-153.
Tuia Devis, Volpi Michele, Volpi Michele, Moser Gabriele (2018), Decision Fusion with Multiple Spatial Supports by Conditional Random Fields, in IEEE Transactions on Geoscience and Remote Sensing
, 56(6), 3277-3289.
Srivastava Shivangi, Vargas Muñoz John E., Lobry Sylvain, Tuia Devis (2017), Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data, in International Journal of Geographical Information Science
Zhu Xiao Xiang, Tuia Devis, Mou Lichao, Xia Gui Song, Zhang Liangpei, Xu Feng, Fraundorfer Friedrich (2017), Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources, in IEEE Geoscience and Remote Sensing Magazine
, 5(4), 8-36.
Rey Nicolas, Volpi Michele, Joost Stéphane, Tuia Devis (2017), Detecting animals in African Savanna with UAVs and the crowds, in Remote Sensing of Environment
, 200, 341-351.
Lefevre Sebastien, Tuia Devis, Wegner Jan Dirk, Produit Timothee, Nassaar Ahmed Samy (2017), Toward Seamless Multiview Scene Analysis from Satellite to Street Level, in Proceedings of the IEEE
, 105(10), 1884-1899.
Courty Nicolas, Flamary Remi, Tuia Devis, Rakotomamonjy Alain (2017), Optimal Transport for Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine Intelligence
, 39(9), 1853-1865.
Volpi Michele, Tuia Devis (2017), Dense semantic labeling of subdecimeter resolution images with convolutional neural networks, in IEEE Transactions on Geoscience and Remote Sensing
, 55(2), 881-893.
Vo A. V., Truong-Hong L., Laefer D. F., Tiede D., Doleire-Oltmanns S., Baraldi A., Shimoni M., Moser G., Tuia D. (2016), Processing of Extremely High Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part B: 3-D Contest, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, 9(12), 5560-5575.
Campos-Taberner Manuel, Romero-Soriano Adriana, Gatta Carlo, Camps-Valls Gustau, Lagrange Adrien, Le Saux Bertrand, Beaupere Anne, Boulch Alexandre, Chan-Hon-Tong Adrien, Herbin Stephane, Randrianarivo Hicham, Ferecatu Marin, Shimoni Michal, Moser Gabriele, Tuia Devis (2016), Processing of Extremely High-Resolution LiDAR and RGB Data: Outcome of the 2015 IEEE GRSS Data Fusion Contest-Part A: 2-D Contest, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, 9(12), 5547-5559.
Tuia Devis, Flamary Rémi, Barlaud Michel (2016), Nonconvex Regularization in Remote Sensing, in IEEE Transactions on Geoscience and Remote Sensing
, 54(11), 6470-6480.
Ofli F., Meier P., Imran M., Castillo C., Tuia D., Rey N., Briant J., Millet P., Reinhard F., et al. (2016), Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response., in Big Data
Wang Qingwang, Gu Yanfeng, Tuia Devis (2016), Discriminative Multiple Kernel Learning for Hyperspectral Image Classification, in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 54(7), 3912-3927.
Tuia D., Persello C., Bruzzone L. (2016), Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances, in IEEE Geoscience and Remote Sensing Magazine
, 4(2), 41.
Tuia Devis, Wegner Jan Dirk, Mallet Clement, Yang Michael Ying (2016), Foreword to the Special Issue on "GeoVision: Computer Vision for Geospatial Applications", in IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
, 9(7), 2840-2843.
Tuia D., Gamba P., Juergens C., Maktav D. (2016), Foreword to the Special Issue on Urban Remote Sensing, in IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens.
Tuia D., Camps-Valls G. (2016), Kernel Manifold Alignment for Domain Adaptation., in PLoS One
Tuia D., Marcos D., Camps-Valls G. (2016), Multi-temporal and multi-source remote sensing image classification by nonlinear relative normalization, in Journal of the ISPRS
Moser G., Tuia D., Shimoni M. (2015), 2015 IEEE GRSS data fusion contest: Extremely high resolution LiDAR and optical data [Technical committees], in IEEE Geoscience and Remote Sensing Magazine
, 3(1), 40-41.
De Morsier F., Tuia D., Borgeaud M., Gass V., Thiran J.-P. (2015), Cluster validity measure and merging system for hierarchical clustering considering outliers, in Pattern Recognition
, 48(4), 1474-1485.
Tuia D., Lopez S., Schaepman M., Chanussot J. (2015), Foreword to the Special Issue on Hyperspectral Image and Signal Processing, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, 8(6), 2337-2340.
Grazioli J., Tuia D., Berne A. (2015), Hydrometeor classification from polarimetric radar measurements: A clustering approach, in Atmospheric Measurement Techniques
, 8(1), 149-170.
Liao W., Huang X., Van Coillie F., Gautama S., Pizurica A., Philips W., Liu H., Zhu T., Shimoni M., Moser G., Tuia D. (2015), Processing of Multiresolution Thermal Hyperspectral and Digital Color Data: Outcome of the 2014 IEEE GRSS Data Fusion Contest, in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
, 8(6), 2984-2996.
Huo L.-Z., Tang P., Zhang Z., Tuia D. (2015), Semisupervised classification of remote sensing images with hierarchical spatial similarity, in IEEE Geoscience and Remote Sensing Letters
, 12(1), 150-154.
Matasci G., Volpi M., Kanevski M., Bruzzone L., Tuia D. (2015), Semisupervised Transfer Component Analysis for Domain Adaptation in Remote Sensing Image Classification, in IEEE Transactions on Geoscience and Remote Sensing
, 53(7), 3550-3564.
Tuia D., Flamary R., Courty N. (2014), Multiclass feature learning for hyperspectral image classification: Sparse and hierarchical solutions, in ISPRS Journal of Photogrammetry and Remote Sensing
, 105, 272-285.
Matasci G., Longbotham N., Pacifici F., Kanevski M., Tuia D. (2014), Understanding angular effects in VHR imagery and their significance for urban land-cover model portability: A study of two multi-angle in-track image sequences, in ISPRS Journal of Photogrammetry and Remote Sensing
, 107, 99-111.
Courty Nicolas, Flamary Remi, Tuia Devis, Rakotomamonjy Alain, Optimal Transport for Domain Adaptation., in IEEE Transactions on Pattern Analysis and Machine Intelligence
The research of this project opens new avenues in remote sensing information fusion. Recently, remote sensing has seen important changes, as many new satellite and airborne sensors have been developed and access to optical information has become much easier. Users are now overwhelmed by the sheer amount of information, mainly but not exclusively optical, at their disposal. New strategies for combining and fusing these sources of information are therefore much needed. The research will develop the fundamental knowledge and the tools to integrate sources of information of either similar or widely different natures, in view of producing more comprehensive and highly-informative products for endusers working in domains such as post-catastrophe assessment, urban development or climate studies. It will consider different data modalities, such as images from different sensors, but also vector layers, in-situ measurements, zonal statistics or user knowledge. This highly-informative sources are nearly always discontinuous and uncertain, which has so far prevented their efficient use in remote sensing. I will propose solutions inspired by advances in machine learning and computer vision, yet specific to the problematics of remote sensing data, to deal with this unreliability and exploit the complementarities and specific strengths of the different sources into integrated fusing schemes.