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.

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).

IGARSS 17

I just came back from IGARSS 17 which took place at Fort Worth, Texas, USA. I did two oral presentations on algorithms dedicated to Water detection in SWOT images:

  • The first one is dedicated to the detection of large water bodies and uses two Markov Random Fields iteratively: the first one do the actual classification, while the second ones estimates the parameters of the class distributions.
  • The other algorithm detects thin water surfaces (such as rivers). It combines a pixel-based detector (which detects parts of the river network) and a higher-level step connecting detected segments.

More information can be found in the proceedings posted in the publication page.

New poster

Last october, I participated to the “Journées CNES Jeunes Chercheurs 2016” (JC2), where I had the chance to give a short talk and present a poster about the work I have done so far at my PhD. It was a great opportunity for me to present my work to talented scientists from very different fields!
As this poster sum up what I have been up to, I thought it would be nice to put it here.

Unfortunately, it is in French… If you only speak english (or want more details!), feel free to take a look at the updated publiactions page!

Here it is: (if the viewer do not work, please click here)

RFIA 2016

I did an oral presentation at RFIA 2016 at the begining of the month.

RFIA is a french conference (held at Clermont-Ferrand this year) about pattern recognition and artificial intelligence. My presentation was on “Un modèle de décomposition pour la détection de changement dans les séries temporelles d’images RSO” and the proceedings can be found in the publication section of this website.