Contract type : Fixed-term contract
Level of qualifications required : Graduate degree or equivalent
Fonction : PhD Position
Level of experience : Recently graduated
About the research centre or Inria department
The Inria Université Côte d’Azur center counts 37 research teams as well as 8 support services. The center’s staff (about 500 people) is made up of scientists of different nationalities, engineers, technicians and administrative staff. The majority of the center’s research teams are located in Sophia Antipolis and five of them are based in an Inria antenna in Montpellier. The Inria branch in Montpellier is growing in size, in accordance with the strategy described in the institution’s Contract of Objectives and Performance (COP).
Context
Within the framework of a partnership
- collaboration between LISAH, CNES and Inria Team Lemon
- Funded by CNES, applications need to be done on CNES website only: https://recrutement.cnes.fr/fr/annonce/2035527-23-114-spatial-data-fusion-and-scaling-for-small-water-reservoir-monitoring-34060-montpellier
Assignment
Fundings: this PhD is half financed by CNES, the other half is being sought by the supervisory team and will be specified later
For more Information please contact the main supervisor : bailly@agroparistech.fr
Application:
Prepare a resumé, a recent transcript and a reference letter from your M2 supervisor/ engineering school director and you will be ready to apply online !
Main activities
Missions:
The water reservoirs smaller than 1.5 ha represent up to 60 % of surface water bodies [France : INPE-MTES] being just a small fraction of the total surface water bodies volume. However these small reservoirs disseminated all over surfaces are of great importance for a smart local use of water (cf. Varennes de l’eau agricole), to preserve ecosystems and to mitigate hydroclimatic risks. Water reservoir monitoring from space is effective or planned (e.g. SWOT) for large reservoirs. However, these missions are not adapted for small water reservoirs since their detection, the monitoring of their water volumes and storage capacities require both very high spatial resolution and high revisiting frequency. Monitoring the water stocks of small reservoirs along seasons is hence still dramatically missing at the global scale. The detection of such small reservoirs is not an issue since their contours can exist in geodatabases (e.g. BD-Topage-France) or it can be processed using a small number of spatially highly resoluted images (Pleiades, Pleiades Neo). Nevertheless, the characterization of their individual geometry and the monitoring of the waterstock is still a challenge.
The main question addressed by the PHD is thus: are we able to monitor (geometry, water volume) small surface water bodies by fusioning spatial data? up to which accuracy, and in which contexts?
To answer this question, the PhD work aims at developing data fusion and spatial downscaling methods allowing the extension to small water bodies of the capacity of contemporain satellite missions to observe waters’ surface (e.g SWOT). Assuming that the maximum contour of any small water body is known, the two main steps of the PhD could be the following. In the first step, spatial downscaling methods will be developed for water surface dynamic estimation at the reservoir scale. This action aims to transform coarse reservoir locations into water surfaces time series with two targeted outputs: water area estimation along time, and water contour along time. This action will be based on “pan-sharpening like” approaches using highly resoluted multispectral images, pixel analytical spectral unmixing optimization, pixel mixing spatial decomposition optimization or convolutional neural networks (super-resolution) techniques. To get water surface spatial contours from coarse pixel resolution (e.g. SurfWater output from Sentinel image series), additive image processing algorithms from vision domain (contours, edges, snakes) are candidates.
A second step aims at transforming water surface dynamics to water storage dynamics at water body scale. Two ways are possible.
A first direct way will estimate an explicit bathymetry by fusionning different informations: water surface and contours dynamics from previous step coupled with altimetric/bathymetric data, spatial laser altimetry (ICESAT-2, GEDI) on water surfaces, very high spatial resolution terrestrial DEM (e.g. CO3D) on water body surroundings or at dry season, and using bathymetric algorithms from image spectra in shallow waters [4SM, Morel 2017]. A spatial estimation method based on spatially constrained radial functions from [Delenne, Bailly et al. 2021] could be extended for that purpose.
A second more indirect way will try to transform water surface dynamics to water volume dynamics with implicit bathymetry through a parametric hypsometric curve with parameters estimated from the same data source as previously exposed.
All methods will be trained and evaluated on a set of contrasted regions (Occitania-France, PACA-France, CapBon-Tunisia, Karnataka-India) benefiting from historical ground truth data (small reservoir water height monitoring at daily time step) and high resolution waterbodies bathymetries (Bathymetric drone available at LISAH-lab). Reference airborne or spatial data with higher resolutions for benchmarks are also available in these test sites: Bathymetric LiDAR (PACA-Fr), Venµs data – SMARA project (CapBon-Tn ; Karnataka-In).
Skills
Applied mathematics
Programming (R, Python)
Benefits package
- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Contribution to mutual insurance (subject to conditions)
General Information
- Theme/Domain : Optimization, machine learning and statistical methods
Biologie et santé, Sciences de la vie et de la terre (BAP A) - Town/city : Montpellier
- Inria Center : Centre Inria d’Université Côte d’Azur
- Starting date : 2023-10-01
- Duration of contract : 1 year, 11 months
- Deadline to apply : 2023-03-14
Contacts
- Inria Team : LEMON
- PhD Supervisor :
Delenne Carole / carole.delenne@inria.fr
About Inria
Inria is the French national research institute dedicated to digital science and technology. It employs 2,600 people. Its 200 agile project teams, generally run jointly with academic partners, include more than 3,500 scientists and engineers working to meet the challenges of digital technology, often at the interface with other disciplines. The Institute also employs numerous talents in over forty different professions. 900 research support staff contribute to the preparation and development of scientific and entrepreneurial projects that have a worldwide impact.
Instruction to apply
Attention! les candidatures sont à faire impérativement sur le site du CNES avant le 16 mars
https://recrutement.cnes.fr/fr/annonce/2035527-23-114-spatial-data-fusion-and-scaling-for-small-water-reservoir-monitoring-34060-montpellier
Attention! Applications must be made on the CNES website before 16 March
https://recrutement.cnes.fr/fr/annonce/2035527-23-114-spatial-data-fusion-and-scaling-for-small-water-reservoir-monitoring-34060-montpellier
Defence Security :
This position is likely to be situated in a restricted area (ZRR), as defined in Decree No. 2011-1425 relating to the protection of national scientific and technical potential (PPST).Authorisation to enter an area is granted by the director of the unit, following a favourable Ministerial decision, as defined in the decree of 3 July 2012 relating to the PPST. An unfavourable Ministerial decision in respect of a position situated in a ZRR would result in the cancellation of the appointment.
Recruitment Policy :
As part of its diversity policy, all Inria positions are accessible to people with disabilities.
