PhD Position: « Self-supervised representation learning from multi-domain data for land cover change detection »

PhD objectives
This PhD aims to investigate new change detection methodologies to map abrupt land-cover conversion changes by exploiting new Satellite Images Times Series (SITS). Land cover conversion changes are defined as landscape transitions between two time periods. To detect and characterize land-cover changes, this PhD aims to discover useful semantic low-dimensional representations from SITS. The PhD work is dedicated to develop new methodologies from a domain adaptation perspective. Cross-domain self-supervised representation learning strategies relying on Deep Generative models will be investigated. First, the multi-view scene analysis will be performed by studying multi-domain image sequences. The term multi-domain refers to a pair of SITS acquired over the same scene but on different periods. In a second stage, the PhD will explore the multi-modal challenge that involves SITS acquired by different sensors.

Context and details: Here

Contacts and submission modalities:
Candidates should send an e-mail to silvia.valero @cesbio.cnes.fr and jordi.inglada @cesbio.eu containing:
Full CV, Motivation letter, contact of 2 references or recommandation letter
Application is open until the position is fulfilled

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