Optical-radar fusion for rip-channel detection from Sentinel data

inria-alumni; Nice; Teletrabalho; EUR 6700 - 10 000; Tempo parcial

Vantagens oferecidas por esta oferta de emprego

Partial reimbursement of public transp

7 weeks annual leave + RTT

Teleworking possibility

Professional equipment available

Social, cultural and sports events

Vocational training

Employer-sponsored health insurance

Resumo da oferta

Inria Côte d’Azur invites a curious intern to contribute to optical–radar fusion for rip-channel detection from Sentinel data. Work with the EVERGREEN team and PhD student on BAISen2 dataset, adding Sentinel-1 SAR data and evaluating detectors using DETR architecture.

The project combines multi-temporal Earth observation, deep learning with PyTorch, and coastal applications. Candidates should have strong Python and ML fundamentals and an interest in remote sensing.

Qualificações

• Advanced Python programming skills.
• Proficiency in deep learning with PyTorch.
• Background in machine learning, computer vision or remote sensing.
• Experience with object detection is appreciated.
• Experience with SAR or satellite data is a plus.

Responsabilidades

• Review the scientific literature on rip-channel and rip-current detection, SAR imagery and optical–radar fusion.
• Collect and preprocess Sentinel-1 SAR data that matches the BAISen2 dataset (same sites and acquisition dates).
• Train and evaluate DETR-like detectors on Sentinel-1 alone and compare them with detectors trained on Sentinel-2.
• Design and implement optical–radar fusion strategies in PyTorch, building on the team’s existing codebase.
• Report and summarise the results.

Conhecimentos

Python

PyTorch

Deep Learning

Remote Sensing

Object Detection

Ferramentas

PyTorch

Descrição da oferta de emprego

Optical-radar fusion for rip-channel detection from Sentinel data

Inria is the French National Institute for Research in Digital Science, of which the Inria Côte d'Azur University Center is a part. With strong expertise in computer science and applied mathematics, the research projects of the Inria Côte d'Azur University Center cover all aspects of digital science and technology and generate innovation. Based mainly in Sophia Antipolis, but also in Nice and Montpellier, it brings together 47 research teams and nine support services. It is active in the fields of artificial intelligence, data science, IT system security, robotics, network engineering, natural risk prevention, ecological transition, digital biology, computational neuroscience, health data, and more. The Inria Center at Université Côte d'Azur is a major player in terms of scientific excellence, thanks to the results it has achieved and its collaborations at both European and international level.

The internship position is opened in the context of the EVERGREEN team project - https://team.inria.fr/evergreen/ .

Our team is actively working on the design and implementation of cutting-edge machine learning techniques to effectively exploit heterogeneous and multi-temporal Earth observation data for agricultrual and enviromental applications.

The team, located in a multidisciplinary laboratory, has an active and stimulating environment with master, PhD and Post-doc students coming from different countries.

The internshipwill be funded by a project in collaboration with INRAE.

Assignments :
With the help of members of the EVERGREEN team (Cassio F. Dantas and Dino Ienco), the intern will add Sentinel-1 SAR data to an existing Sentinel-2 dataset for rip-channel detection (BAISen2), which was developed within the context of an ongoing PhD thesis in the team (Christopher Jabea). The intern will then assess what SAR imagery brings to the detection of rip channels along the French Atlantic coast, using deep learning detectors based on the DETR architecture and optical-radar fusion.

For a better knowledge of the proposed research subject :

Rip channels are deep incisions in surf-zone sandbars associated with rip currents, the leading cause of drowning on beaches worldwide [1]. Our team built BAISen2 [2], an open dataset of 2,058 Sentinel-2 patches with 12,504 bounding boxes. It covers 42 sites along 350 km of the French Atlantic coast from 2015 to 2024.

Optical imagery depends on daylight and cloud cover, but SAR does not, and rip-current signatures have already been seen in spaceborne SAR imagery [3]. The internship will address three questions:

• Are rip channels visible in Sentinel-1 imagery?
• How does a detector trained on Sentinel-1 alone compare with one trained on Sentinel-2?
• Does combining Sentinel-1 and Sentinel-2 improve detection [4]?

The team’s existing codebase for training and evaluating DETR-like detectors will be extended to optical–radar fusion.

[1] B. Castelle, T. Scott, R. W. Brander, R. J. McCarroll. “Rip current types, circulation and hazard.” Earth-Science Reviews, 163:1–21, 2016.

[2] C. Jabea, J. Turpin, C. F. Dantas, B. Castelle, I. Manighetti, D. Ienco. “BAISen2: A Sentinel-2 image dataset for rip channels detection along the French Atlantic coast.” Zenodo, 2026. https://doi.org/10.5281/zenodo.21980065

[3] J. C. B. da Silva. “SAR observation of rip currents off the Portuguese coast.” In V. Barale et al. (eds.), Remote Sensing of the European Seas, pp. 399–410. Springer, 2008.

[4] A. Fuller, K. Millard, J. R. Green. “CROMA: Remote sensing representations with contrastive radar-optical masked autoencoders.” NeurIPS, 2023.

Collaboration :
The intern will work closely with Christopher Jabea, a PhD student in the EVERGREEN team who works on rip-channel detection from satellite imagery and is the lead author of the BAISen2 dataset

Main activities :

• Review the scientific literature on rip-channel and rip-current detection, SAR imagery and optical–radar fusion.
• Collect and preprocess Sentinel-1 SAR data that matches the BAISen2 dataset (same sites and acquisition dates).
• Train and evaluate DETR-like detectors on Sentinel-1 alone and compare them with detectors trained on Sentinel-2.
• Design and implement optical–radar fusion strategies in PyTorch, building on the team’s existing codebase.
• Report and summarise the results

Additional activities:

• Contribute an extension of the BAISen2 dataset with Sentinel-1 data, to be released openly.
• If the results allow, prepare a scientific paper for submission to an international journal or conference.

Technical skills and level required:

• Advanced Python programming skills
• Proficiency in deep learning with PyTorch
• Background in machine learning, computer vision or remote sensing
• Experience with object detection is appreciated
• Experience with SAR or satellite data is a plus

Languages:

• French: a plus but not necessary

Relational skills:

• Ability to work both on your own and in a team
• Communication skills, including presenting research results and working in an interdisciplinary team
• Analytical thinking and problem solving
• Curiosity and a continuous-learning mindset

Other valued skills:

• Interest in Earth observation and coastal applications

Avantages

• 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 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
• Employer-sponsored health insurance plan

We are looking for a curious and rigorous student who enjoys working with real-world satellite data and is motivated by applications with a societal impact, such as beach safety. Solid skills in deep learning with PyTorch are essential. An interest in Earth observation and a willingness to explore a new modality (SAR) are real assets, and you don’t need to be a radar expert.