Presentation on Visual Question Answering for Remote Sensing – ISPRS virtual event 2020

We gave a presentation on better generic object counting in the framework of Visual Question Answering for Remote Sensing (RSVQA) at the virtual event of the 2020 ISPRS congress.

RSVQA is a new project which aims at changing the way we extract information from remote sensing images. If you missed the presentation, you can watch it here:

If you want to know more about RSVQA, you can consult the project’s page (including links to related articles) or download the conference paper.

Tutorial on Deep Learning for Remote Sensing at NoRSC 19

On the 16th of September, I gave a workshop on Deep Learning for Remote Sensing together with Ahmed Samy Nassar from IRISA in France. This workshop was given at the Nordic Remote Sensing Conference (NoRSC) which took place in the Aarhus Institute of Advanced Studies, Denmark. In addition to Ahmed, this workshop was also prepared by Diego Marcos.

The first part of the tutorial was a general introduction on Deep learning, which lead to two practicals (one on classification, and the second on semantic segmentation) focused on remote sensing data. Both practicals were made using Jupyter notebooks, which the attendees can reuse to bootstrap their next project!

We would like to thank the 36 people who attended this workshop!

New paper in IJGIS!

With Shivangi Srivastava, John Vargas and Devis Tuia, we recently published an article on land-use classification using ground based pictures (e.g. Google Street View images):

Fine-grained landuse characterization using ground-based pictures: a deep learning solution based on globally available data

Shivangi Srivastava, John Vargas, Sylvain Lobry, Devis Tuia

in International Journal of Geographical Information Science (IJGIS)

Good news: it is published in open access!

GSV Images

Ground-based pictures (both inside and outside) from Google Street View.

This work assigns a land-use class (e.g. religious place, hotel, post office, …) to buildings, which is an essential task towards better urban land management. In order to do this classification, we use the images from Google Street View pointing to a building as an input to a variable input siamese network (allowing to take into account the information coming from an unknown number of images). This network has been trained using ground truth labels extracted from OpenStreetMap (which are freely available).

PhD award of the Fondation Mines-Télécom

I have been awarded best PhD thesis from the Fondation Mines-Télécom!

Every year, a prize honors the best PhD thesis from the Futur & Ruptures program (co-funded by the Fondation Mines-Télécom and the Carnot institute Télécom & Société numérique).


After presenting the work I have done during my PhD at the Journée Futur & Ruptures (a day organized every year by the Fondation Mines-Télécom to present the work achieved during the PhD they funded), four of us were selected as finalists for the PhD award.

I then made a video (of 3 minutes, in french) summing up my work (long way to go before being a youtuber..!). The prizes were announced during the special event organized by the Fondation, which was held on the 27th of March at the BNP Parisbas headquarters in Paris.

Credit: Franck Beloncle


The awards were given as follow:

1st prize: Sylvain Lobry – Markovian models for information extraction in SAR imagesTélécom ParisTech

2nd prize (tied):

El Mehdi Amhoud – Coding Techniques for Space Division Multiplexed Optical Fiber Systems – Télécom ParisTech

Yousra Bekhti – Source localization for functional brain imaging with M/EEG – Télécom ParisTech

Ivan Gorynin – Fast Filtering and Unsupervised Estimation in Switching Markov Models Télécom SudParis

It was a great evening, and I particularly enjoyed the interactions with the other recipients! Once again, I would like to thank the Fondation for this prize, as well as Loïc Denis, Florence Tupin, Roger Fjørtoft and Cyrielle Flosi for their advices on the video.

Posted in PhD