Nicolas Audebert

About me

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I am computer vision, machine learning and remote sensing researcher. I prepared my PhD in Computer Science at ONERA and IRISA, supervised by Sébastien Lefèvre and Bertrand Le Saux. I teach computers how to look at airborne images to create beautiful maps. My research focuses on deep neural networks (deep learning) for remote sensing image analysis and Earth Observation. Specifically, I am currently working on convolutional neural networks for semantic labeling of airborne and satellite images (RGB, multi and hyperspectral).

I am interested in machine learning, algorithms and Python.


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



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

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

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.

Before this, I studied applied and fundamental mathematics at Université Paris-Sud and spent three years in preparation classes for the competitive entrance to engineering schools at lycée Janson-de-Sailly (Paris), with a heavy focus on maths and physics.

During the summer 2014, I interned in Withings' Platform team (web development/data analysis) where I designed and implemented an automated incentive system to help people take care of their health (PHP/Backbone.js).

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.

Since October 2015, I am a PhD student at ONERA.