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Number of documents

10

Nicolas Audebert


I am Computer Vision and Machine Learning reseracher with a focus on multi-modal data and remote sensing.

I hold a PhD in Computer Science from the University of South Brittany (France), prepared jointly between ONERA and IRISA, My thesis was supervised by Sébastien Lefèvre and Bertrand Le Saux. It dealt with deep neural networks (deep learning) for remote sensing image analysis and Earth Observation. Specifically, I have worked on convolutional neural networks for semantic labeling of airborne optical images (infrared, RGB and hyperspectral).

I am currently a research scientist in the industry.

I am interested in machine learning, algorithms and Python.

Take a look at my personal website for more information.


ONERA - The French Aerospace Lab [Palaiseau]   

Journal articles1 document

  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks. ISPRS Journal of Photogrammetry and Remote Sensing, Elsevier, 2018, 140, pp.20-32. ⟨10.1016/j.isprsjprs.2017.11.011⟩. ⟨hal-01636145⟩

Conference papers9 documents

  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Couplage de données géographiques participatives et d'images aériennes par apprentissage profond. GRETSI, 2017, Juan-les-Pins, France. ⟨hal-01672870⟩
  • Nicolas Audebert, Alexandre Boulch, Hicham Randrianarivo, Bertrand Le Saux, Marin Ferecatu, et al.. Deep learning for urban remote sensing. Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. ⟨10.1109/JURSE.2017.7924536⟩. ⟨hal-01672854⟩
  • Amina Ben Hamida, Alexandre Benoit, P. Lambert, L Klein, Chokri Ben Amar, et al.. DEEP LEARNING FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES WITH RICH SPECTRAL CONTENT. IEEE International Geoscience and Remote Sensing Symposium, Jul 2017, Fort Worth, United States. ⟨hal-01654187⟩
  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper). Joint Urban Remote Sensing Event (JURSE), Mar 2017, Dubai, United Arab Emirates. ⟨10.1109/JURSE.2017.7924566⟩. ⟨hal-01438499⟩
  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps. EARTHVISION 2017 IEEE/ISPRS CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Jul 2017, Honolulu, United States. ⟨10.1109/CVPRW.2017.199⟩. ⟨hal-01523573⟩
  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Réseaux de neurones profonds et fusion de données pour la segmentation sémantique d'images aériennes. ORASIS, GREYC, 2017, Colleville-sur-Mer, France. ⟨hal-01672871⟩
  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. On the usability of deep networks for object-based image analysis. International Conference on Geographic Object-Based Image Analysis (GEOBIA), Sep 2016, Enschede, Netherlands. ⟨hal-01320010⟩
  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?. IEEE International Geosciences and Remote Sensing Symposium (IGARSS), Jul 2016, Beijing, China. ⟨10.1109/IGARSS.2016.7730327⟩. ⟨hal-01320016⟩
  • Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre. Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks. Asian Conference on Computer Vision (ACCV16), Nov 2016, Taipei, Taiwan. ⟨10.1007/978-3-319-54181-5_12⟩. ⟨hal-01360166⟩