Nicolas Audebert

Nicolas Audebert

Maître de conférences
Personal website: https://nicolas.audebert.at
Office: 37.1.41

Nicolas Audebert is an assistant professor in computer science at the Conservatoire National des Arts & Métiers (CNAM) since 2019. He conducts his research at the CEDRIC laboratory (Research and Studies Center on Computer Science and Communications). His work focuses on machine learning for artificial perception, especially for Earth Observation applications. He defended a PhD in computer science in 2018, prepared at ONERA and University of South Britanny, advised by Dr Bertrand Le Saux (ONERA) and directed by Pr Sébastien Lefèvre (UBS/IRISA). His PhD dealt with the design and the development of deep neural networks to automatically generate high-quality maps from aerial and satellite data (pictures, multispectral and hyperspectral images, Lidar data). He introduced several data fusion architectures to leverage multimodal sensors and improve the overall accuracy of the computer-generated maps.

Publications

2020

Articles de revue

  1. rambour, c.; Audebert, N.; Koeniguer, E.; Le Saux, B.; Crucianu, M. and Datcu, M. Flood detection in time series of optical and sar images. In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B2-2020: 1343-1346, 2020. doi  www 

Articles de conférence

  1. Dubucq, D.; Audebert, N.; Achard, V.; Alakian, A.; Fabre, S.; Credoz, A.; Deliot, P. and Le Saux, B. A real-world hyperspectral image processing workflow for vegetatotion stress and hydrocarbon indirect detection. In XXIV ISPRS Congress, Nice, France, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020, 2020. doi  www 

2019

Articles de revue

  1. Audebert, N.; Saux, B. Le and Lefèvre, S. Deep Learning for Classification of Hyperspectral Data: A Comparative Review. In IEEE geoscience and remote sensing magazine, 7 (2): 159-173, 2019. doi  www 
  1. Audebert, N.; Boulch, A.; Le Saux, B. and Lefèvre, S. Distance transform regression for spatially-aware deep semantic segmentation. In Computer Vision and Image Understanding, 189: 102809, 2019. doi  www 

Articles de conférence

  1. Castillo-Navarro, J.; Audebert, N.; Boulch, A.; Le Saux, B. and Lefèvre, S. What Data are needed for Semantic Segmentation in Earth Observation?. In 2019 Joint Urban Remote Sensing Event (JURSE), pages 1-4, IEEE, Vannes, France, 2019. doi  www 
  1. Audebert, N.; Herold, C.; Slimani, K. and Vidal, C. Multimodal deep networks for text and image-based document classification. In Conférence Nationale sur les Applications Pratiques de l'Intelligence Artificielle (APIA), Toulouse, France, 2019. www 

2018

Articles de revue

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks. In ISPRS Journal of Photogrammetry and Remote Sensing, 140: 20-32, 2018. doi  www 

Articles de conférence

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples. In International Geoscience and Remote Sensing Symposium (IGARSS 2018), Valencia, Spain, 2018. doi  www 
  1. Audebert, N.; Boulch, A.; Le Saux, B. and Lefèvre, S. Segmentation sémantique profonde par régression sur cartes de distances signées. In Reconnaissance des Formes, Image, Apprentissage et Perception (RFIAP), Marne-la-Vallée, France, 2018. www 
  1. Huang, B.; Lu, K.; Audebert, N.; Khalel, A.; Tarabalka, Y.; Malof, J.; Boulch, A.; Le Saux, B.; Collins, L.; Bradbury, K.; Lefèvre, S. and El-Saban, M. Large-scale semantic classification: outcome of the first year of Inria aerial image labeling benchmark. In IGARSS 2018 - IEEE International Geoscience and Remote Sensing Symposium, pages 1-4, Valencia, Spain, 2018. doi  www 

2017

Articles de revue

  1. Boulch, A.; Guerry, J.; Le Saux, B. and Audebert, N. SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks. In Computers and Graphics, 71: 189-198, 2017. doi  www 

Articles de conférence

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Couplage de données géographiques participatives et d'images aériennes par apprentissage profond. In GRETSI, Juan-les-Pins, France, 2017. www 
  1. Audebert, N.; Boulch, A.; Randrianarivo, H.; Le Saux, B.; Ferecatu, M.; Lefèvre, S. and Marlet, R. Deep learning for urban remote sensing. In Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates, 2017. doi  www 
  1. Ben Hamida, A.; Benoit, A.; Lambert, P.; Klein, L; Ben Amar, C.; Audebert, N. and Lefèvre, S. DEEP LEARNING FOR SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGES WITH RICH SPECTRAL CONTENT. In IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, United States, 2017. www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Fusion of Heterogeneous Data in Convolutional Networks for Urban Semantic Labeling (Invited Paper). In Joint Urban Remote Sensing Event (JURSE), Dubai, United Arab Emirates, Joint Urban Remote Sensing Event 2017 , 2017. doi  www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps. In EARTHVISION 2017 IEEE/ISPRS CVPR Workshop. Large Scale Computer Vision for Remote Sensing Imagery, Honolulu, United States, 2017. doi  www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Réseaux de neurones profonds et fusion de données pour la segmentation sémantique d'images aériennes. In ORASIS, Colleville-sur-Mer, France, 2017. www 

Non publié

  1. Chan-Hon-Tong, A. and Audebert, N. Pointing is sufficient for remote sensing images. , working paper or preprint. doi  www 

2016

Articles de conférence

  1. Audebert, N.; Le Saux, B. and Lefèvre, S. On the usability of deep networks for object-based image analysis. In International Conference on Geographic Object-Based Image Analysis (GEOBIA), Enschede, Netherlands, 2016. www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. How Useful is Region-based Classification of Remote Sensing Images in a Deep Learning Framework?. In IEEE International Geosciences and Remote Sensing Symposium (IGARSS), Beijing, China, 2016. doi  www 
  1. Audebert, N.; Le Saux, B. and Lefèvre, S. Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks. In Asian Conference on Computer Vision (ACCV16), Taipei, Taiwan, 2016. doi  www 
Top