Working group 3: Pierre Lepetit – Estimation of visibility and snow height on webcam images with learning to rank approach

L’Atelier interne « SCAI & AI4Climate » réunit les chercheurs, ingénieurs, doctorants, post doctorants concernés par les thématiques liées à conception et l’utilisation de nouvelles méthodes d’Intelligence Artificielle pour l’étude de l’environnement, allant du modèle à l’observation. Les premières réunions seront consacrées aux travaux des doctorants. L’exposé sera suivi d’une discussion avec les participants sur l’approche et les perspectives possibles du travail. 

16 Mars à 10h
sur le campus de Jussieu,
Salle de réunion SCAI
Batiment Esclangon 1er étage

Participer à la réunion Zoom
(voir information de connexion ci-dessous)

  • The image-based estimation of meteorological parameters provides clear benefits for surface weather observation. When a local event arises, as a dense fog or a snow settling, webcams and CCTV cameras are sources of valuable information. These images actually inform about the class of weather (sunny, rainy, foggy, snowy, etc). They also enable to gauge quantitative parameters as the horizontal visibility (the farest you can see), the snow height, the precipitation rate, etc, with a variable precision.
  • Recently, the weather classification task has been successfully addressed by deep learning approaches. However, the quantitative estimation faces a strong difficulty: the existing data sets that contain both images and precise weather measurements are rare and involve only few different outdoor scenes. It is virtually impossible for an expert to assign image-wise quantitative labels, but it is possible to compare two images from the same webcam and therefore assign pairwise labels. An “uncomparable” label being assigned to couples for which the expert is not able to distinguish the two images with respect to the parameter.
  • This analysis gives the starting point of the workshop. The discussion will deal with the methods of labeling, learning to rank and calibration that may help to yield such comparisons and to predict ordinal or quantitative estimations of visibility and snow height. The way uncomparable pairs could lead to predict an image-wise uncertainty will also be addressed.

Participer à la réunion Zoom

https://zoom.us/j/98278319724

ID de réunion : 982 7831 9724

Trouvez votre numéro local : https://zoom.us/u/agSnuNJYM

Internship offers 2020

Filled internship:

The following list of internship has already be filled for year 2019-2020

Groupe de travail 1 : Evangelos Moscos

Quand : 4 Décembre 2019 à 14:00

Où : Salle de réunion du LOCEAN, Tour 45-55, 4ème étage

Evangelos Moscos (LMD,LIP6) présentera sa problématique de recherche qui porte sur « Identification de structures tourbillonnaires dans la Méditerranée par Deep Learning ». Le sujet aborde des problèmes méthodologiques liés à la détection de structures à différentes échelles qui se retrouvent dans de nombreux problèmes.

 L’exposé sera suivi d’une discussion avec les participants sur l’approche et les perspectives possibles du travail.

 Le groupe de travail interne « SCAI & AI4Climate » réunit les chercheurs, ingénieurs, doctorants, post doctorants concernés par les thématiques liées à conception et l’utilisation de nouvelles méthodes d’Intelligence Artificielle pour l’étude de l’environnement, allant du modèle à l’observation. Les premières réunions seront consacrées aux travaux des doctorants qui commencent leur thèse cette année dans ce cadre.

20 March 2019: Seminar AI in LOCEAN

Future seminar in LOCEAN (open to everyone) is directly related to AI & Climate:

Redouane Lguensat (Université Grenoble Alpes, IGE, Grenoble): Inferring hidden equations using Quasi-Geostrophic theory guided machine learning

mer., 20 mars, 11:00 – 12:00
LATMOS, grande salle du 4ème, Tour 45-46, pièce 411

Abstract:

Inferring hidden equations governing dynamical systems from data has always been one of the challenging problems in the interplay between physics and data science. It was just a matter of time before the recent advancements in machine learning and in computational capacities come in hand and spark off a series of works dedicated to address this problem. In this work we present a Quasi-Geostrophic numerical model coded using differentiable operators thus permitting the use of automatic differentiation libraries (e.g. Tensorflow). This makes the model flexible and suited for parameter optimization, especially using neural networks. We illustrate the relevance of the proposed architecture through an example of a regression problem where we show how can we obtain the parameters of the potential vorticity equation using only consecutive scenes of Sea Surface Height. This can be of interest for finding the closest QG-like approximation to a given ocean simulation model or help exploring the effect of adding new operators in the potential vorticity equation. The code we provide is suitable for GPU implementation and therefore can allow for faster execution and profit from the quick advancements in GPU development. We expect that the directions of research we suggest will help in bringing more interest in applied machine learning to ocean numerical modeling.