Nicolas Audebert

About me

I am a computer vision, machine learning and remote sensing researcher. I hold a PhD in Computer Science prepared at ONERA and IRISA, supervised by Sébastien Lefèvre and Bertrand Le Saux. My thesis was about teaching computers how to look at airborne images to create beautiful maps. My research now focuses on deep neural networks (deep learning) for artificial perception, ranging from document image analysis to remote sensing and Earth Observation. Specifically, I am currently working on multimodal learning for image/text understanding in administrative documents.

I am interested in machine learning, algorithms and Python.


April 2019: I will be presenting at the GdR ISIS meeting on weakly and semi-supervised learning for image and video classification. My talk will detail some of the work I did at Quicksign on image/text clustering for document recognition.

April 2019: Our review on deep convolutional and recurrent neural networks for hyperspectral image classification has ben accepted for the IEEE Geoscience and Remote Sensing special issue on hyperspectral data. Preprint here.

October 2018: I successfully defended my PhD thesis! The manuscript (in french) is available here with slides.

July 2018: I was at IGARSS'18 in Valencia, where I presented our work on generative adversarial network for hyperspectral samples synthesis. You can find the code here!

March 2018: We have one paper accepted for IGARSS 2018 on generative adversarial networks for hyperspectral data synthesis. We'll also appear on the Inria Aerial Image Labeling benchmark write-up on building extraction.

January 2018: I ported the code of our deep network for aerial/satellite semantic segmentation to PyTorch for an easier use: fork it on GitHub!

November 2017: Our latest journal paper on data fusion for remote sensing data using deep fully convolutional networks is out !

July 2017: I was at CVPR 2017 for the Earthvision workshop, where I presented our work on semantic mapping using deep nets and OpenStreetMap data.

June 2017: I collaborated with the LISTIC team on using deep nets to perform semantic segmentation on Sentinel-2 images. This work will be presented at IGARSS'17 in Forth Worth, Texas.

June 2017: I presented at ORASIS 2017 our work on data fusion with deep networks for remote sensing (slides).

May 2017: Our submission on joint deep learning using optical and OSM data for semantic mapping of aerial/satellite images has been accepted to the EarthVision 2017 CVPR Workshop !

April 2017: Our Remote Sensing journal paper on vehicle segmentation for detection and classification is out in open access on the MPDI website.

March 2017: My colleague Alexandre Boulch will present the SnapNet architecture for semantic segmentation of unstructured point clouds at Eurographics 3DOR workshop. It is the current state-of-the-art on the Semantic3D dataset (code).

March 2017: Our paper on data fusion for remote sensing using deep nets won the 2nd best student paper award at JURSE 2017 ! Slides and poster are available.

Februrary 2017: The code of the deep network we used for the ISPRS Vaihingen 2D Semantic Labeling Challenge is out on Github !

January 2017: We will present two invited papers at JURSE 2017 !

November 2016: I will be at ACCV'16 in Taipei to present our poster on semantic segmentation of Earth Observation using multi-scale and multimodal deep networks.

October 2016: I will be at PyCon-fr (the French Python conference) to speak about deep learning using Python (slides (in French) and video (in French, too)).

September 2016: Our paper on the use of deep networks for object-based image analysis of vehicles in the ISPRS dataset has been distinguished by the "Best Benchmarking Contribution Award" at GEOBIA 2016 !

September 2016: I will be at GEOBIA 2016 in Enschede to talk about our work on object-based analysis of cars in remote sensing images using deep learning.

September 2016: Our paper on semantic segmentation for Earth Observation was accepted at ACCV'16 for a poster presentation. Check out the state-of-the-art results on the ISPRS Vaihingen 2D Semantic Labeling Challenge !

July 2016: I will be at IGARSS'16 in Beijing to present our work on superpixel-based semantic segmentation of aerial images.

April 2016: Our paper on region-based classification of remote sensing images using deep features has been accepted at IGARSS'16 for an oral presentation.

October 2015: I started as a PhD student at ONERA and IRISA.


Most of my papers should be in open access on HAL or on arXiv.

Journals :

Conferences :

Communications :

Talks :


ENPC : First year main course of C++ programming

ENPC : First year algorithmic and data structures


Professionnal experiences

January 2019 - today Research scientist Quicksign Deep learning for document image analysis: image and text classification, OCR, semi-supervised & continuous learning.
April - September 2015 Research internship TUM - Computer Vision Lab Deep learning for facial expression recognition
June - September 2014 Software Engineer (intern) Withings Web development of an incentive platform for quantified self (PHP/Backbone.js)
2013 - 2015 Software Engineer J2S Web development for several J2S clients (PHP/JavaScript)
2012 - 2015 Member Supélec Rézo Sysadmin and software development for the organization providing Internet access to 700 students


2015 - 2018 PhD graduate - "Classification of big remote sensing data"
Deep learning for image processing and Earth Observation
ONERA, The French Aerospace Lab
Institute for Research in Computer Science and Random Systems (IRISA)
2014 - 2015 Master Human-Computer Interaction Université Paris-Sud
2012 - 2015 Engineering student - Computer Science Supélec
2012 - 2013 Bachelor Mathematics Université Paris-Sud

Skills :

Programming : Tools : Languages :

I graduated in 2015 from Supélec, one of France's top engineering school, with a focus on Computer Science. I also graduated from the université Paris-Sud with a MSc in Human-Computer Interaction. While I was an engineering student, I was head of the Supélec Rézo organization that provides Internet access to all Supélec' students.

I did my final intership in the Computer Vision lab at TUM under Pr. Daniel Cremers supervision. I worked on deep learning for facial expression recognition.

From October 2015 to October 2018 I was a PhD candidate at ONERA.

Since January 2019 I am a research scientist at Quicksign.