ACDC with deep learning : Automatic Crater Detection and Characterization

29/04/2022 – 30/04/2022 all-day

Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : Université Paris-Saclay, Centrale SUPELEC
Durée : 4-6 months + 3 years
Contact :
Date limite de publication : 2022-04-29

Contexte :
This study takes place in the data deluge from the numerous space missions across the Solar System. The project proposes to develop a tool to automatically detect and characterize the most ubiquitous feature on planetary body : craters.
The aim is to developed a tool to define precise size and position of all craters in the scene, whatever the illumination conditions, the type of sensor and the scale. As a second goal, the project will have to determine the crater characteristics, such primary / secondary (ejecta from a previous impact, not from a direct impactor), presence / absence of rays, erosion level…

Sujet :
This study will take advantage of the machine learning and deep learning libraries available as open source to propose the most versatile and robust detection method. We propose to develop a new tool dedicated to this task. Such software pipeline is required to tackle fundamental questions in planetary science to study the surface processes across the Solar System. It will be a crucial tool to precisely date the surface and open a new era for onboard decisions on landing or targeting, to maximize the science return of future deep space missions.

The internship subject should continue in PhD and will take place in collaboration between planetary scientist and IA expert within University Paris-Saclay/Centrale SUPELEC.

Profil du candidat :
The candidate must have a engineer or master grade in machine learning/data mining or in astronomy/planetary science. Double competence in both fields will be encouraged.

Formation et compétences requises :
An excellent level of programming skills is required (Python, Linux). We expect the candidate to have a good level of communication in English (written and oral).

Adresse d’emploi :
Campus Université Paris-Saclay