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Annonce

8 décembre 2020

Deep learning for multi-modal satellite remote sensing


Catégorie : Ingénieur


Open position - 15 month contract

 

About CESBIO
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Research at CESBIO aims to develop knowledge on continental biosphere
dynamics and functioning at various temporal and spatial scales and as
such participates in the specification of space missions and the
processing of remotely sensed data. CESBIO is or has been PI for 2 ESA
satellite missions (SMOS, the Soil Moisture and Ocean Salinity
satellite, and BIOMASS, a P-band SAR system to be launched in 2022)
and for the French-Israeli Venus satellite (2-day revisit, 10 m
resolution, optical sensor for vegetation monitoring, launched in
2017). CESBIO has developed the [`iota2'] processing chain for the
operational production of land-cover maps at the national French
scale. It has therefore a strong experience in upscaling learning and
classification processes. CESBIO has been committed over the last two
years in collecting feedback, tailoring `iota2' outputs for various
end-users, and disseminating it for several research institutes in
France.


[`iota2'] <https://framagit.org/iota2-project/iota2>


Context
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The [MAESTRIA] project (Multi-modAl Earth obServaTion Image Analysis)
aims to solve the methodological challenges related to the fully
automatic analysis of the massive amount of images acquired by Earth
Observation platforms. MAESTRIA targets to generate land-cover and
land-use descriptions at country scale at many spatial resolutions and
sets of classes. The ultimate goal is to provide a continuum of
spatially and semantically consistent products, that are relevant for
many end-users and applications. Both public policies at local or
national levels and scientific models will benefit from such kinds of
products for climate modelling, urban planning, crop monitoring or
impact assessment of surface changes.

The output of the MAESTRIA project will be two-fold: (i) methods that
leverage current challenges in Earth Observation image analysis; (ii)
a large range of precise and up-to-date land-cover maps available over
very large scales from 2m to 100m. Both will be made freely available
so as to stimulate research and commercial services built upon such
maps.

The current position integrates in and is funded by the MAESTRIA
project.


[MAESTRIA] <https://maestriaproject.github.io/>


Assignment
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The work is dedicated to the fusion of heterogeneous information
coming from different satellite sensors in order to improve the
accuracy and semantic richness of the produced land cover maps.

In MAESTRIA, a new /pivotal/ representation of the multi-modal data
will be developed in order to minimize the loss of information with
respect to the original data: *a set of common variables to all
modalities sampled at 10m resolution and daily revisit*. Two main
approaches will be developed in parallel: one based on (1) *physical
approaches* (models of the landscapes and the measuring mechanisms)
and the other one based on purely (2) *statistical approaches*. We
will pay special attention to the possibility of cross-pollination of
the two approaches.

The specific tasks of the job cover the implementation and the
evaluation of representation learning algorithms (deep learning
networks). The work will be done under the supervision of the MAESTRIA
researchers.


Skills
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• Master's or PhD in Applied Mathematics, Computer Science or Machine
Learning
• Good programming skills in Python, knowledge of Pytorch will be
highly appreciated


Application procedure
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Candidates should send an e-mail to jordi.inglada@cesbio.eu
containing:
1. Full CV
2. Letter of interest
3. Contact information for 2 references

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(c) GdR 720 ISIS - CNRS - 2011-2020.