A large number of oceanographic applications rely on gridded maps of Sea Surface Height (SSH) and related variables such as surface currents. Such products have been for about 20 years designed by the DUACS (Data Unification and Altimeter Combination System) project, led by the Collecte Localisation Satellites company (CLS, Toulouse) in partnership with CNES. The DUACS system, unique worldwide, processes uncalibrated along-track altimetric observations into blended ocean products. More than 6,000 users have been registered so far. The current DUACS system processes constellations of 2 to 4 nadir-looking altimeters. The algorithm is based on a static, statistical interpolation of along-track data and delivers SSH daily maps with a resolution of 1/4°, resolving scales larger than 200 km. However, recent research has unveiled an important climatic role of finer-scale ocean surface dynamics, and motivates building higher resolution products. Concomitantly, the Surface Water and Ocean Topography (SWOT) wide-swath altimetry mission, to be launched in 2021, will provide 120-km wide SSH images at a kilometric resolution. This paves the way to gridded products with a resolution much higher than today. Updating the DUACS system to increase resolution and incorporate SWOT will be challenging for several reasons, mostly related to SWOT: high spatial resolution, low temporal resolution, noisy measurements, huge amount of data. There is a general consensus, among the SWOT Science Team, that the next-generation algorithms for building high-resolution gridded products from space altimetry, including SWOT, will involve Numerical Ocean Circulation Models (NOCM) and Data Assimilation (DA) techniques instead of statistical interpolation. The project aims at investigating approaches based on NOCM and DA methods to increase the spatial resolution of, and include the future SWOT data in the DUACS system. The strategy is to (i) set up a working framework with simulated SSH data, data denoising techniques, and evaluation metrics; (ii) identify the optimal NOCM to merge altimetric data, including SWOT; (iii) examine and implement the appropriate NOCM/DA system; (iv) evaluate and compare the developed system to the existing DUACS system; and (v) communicate toward the user community through a dedicated workshop. Unlike operational systems, where most computational resources are allocated to a primitive-equation model for prediction purposes, the present system will be designed to extend the observations in the first place, therefore putting most efforts into data processing and assimilation. The consortium primarily results from a new collaboration between the Oceanography team (MEOM) of the Institut des Géosciences de l'Environnement (IGE, Grenoble) and CLS. MEOM concentrates high expertise in ocean physics, numerical modeling, and DA. CLS expertise essentially concerns the operational processing and exploitation of altimetry data. The project will also benefit from external expertise in image processing and machine learning techniques. The project will benefit to society and/or economy for a better understanding of the climate machine, for industrial applications at sea, and for non-profit applications such as transport of pollutants, slick drift predictions, or rescue at sea. The scientific results will be disseminated through science publications and communications. The tools developed will feed back the operational DUACS system and, at mid-term, the Copernicus Marine Environment Monitoring Service (CMEMS). Both partners will benefit from the academic research transfer towards applications and will have started a new, potentially long-lasting collaboration.

The surveillance of the maritime traffic is a major issue for defense contexts (e.g., surveillance of specific zones, borders,...) as well as security and monitoring contexts (e.g., monitoring of the maritime traffic, of fisheries activities). Spaceborn technologies, especially satellite ship tracking from AIS messages (Automatic Identification System) and high-resolution imaging of sea surface, open new avenues to address such monitoring and surveillance objectives. However, operational systems cannot currently fully process these satellite-derived data streams. For instance, the CRM (Centre de Renseignement de la Marine) evaluates that less than 20% of the overall AIS data (about a few tens of millions of AIS messages daily) are actually analysed for abnormal behaviour detection. Besides, the free access to Sentinel earth observation data streams (high-resolution SAR and optical imaging, up to a few Tb daily) offers novel opportunities for the analysis and detection of ship behaviours, including AIS-Sentinel data synergies. In this context, SESAME initiative aims at developing new big-data-oriented approaches to deliver novel solutions for the management, analysis and visualization of multi-source satellite data streams. Targeted at the automatic generation and documenting of early warnings (both in real-time and re-analysis modes), the key scientific and technological challenges cover the development of hardware and software platforms adapted to the volume and the features of the data streams to be processed along with the design of novel models and algorithms for AIS-Sentinel synergies and the automatic detection and recognition of abnormal behaviours. The originality of the project lies in a big-data approach to jointly address these challenges based on the complementarity of the scientific expertise gathered in the consortium: big-data platforms, mining strategies for time series, modeling and analysis of tracking data, Sat-AIS signal analysis, high-resolution satellite imaging. It involves four main scientific and technical tasks: Hardware and software platforms for the management, processing and visualization of multi-source satellite data streams for maritime traffic surveillance (Task 1), Analysis, modeling and detection of marine vessel behaviours from AIS data streams (Task 2), AIS-Sentinel data synergies for maritime traffic surveillance (Task 3), Visualization and mining of large-scale augmented marine vessel tracking databases (Task 4). A fifth task embeds the implementation of the proposed solutions for dual case-studies representative of the scientific and technical objectives targeted by the project. For the specification of the case-studies as well as the critical assessment phase at the end of the project, we will invite additional external thematic expertise to the consortium (e.g., EMSA, European Maritime Safety Agency). SESAME initiative, from its consortium formed by three academic members (Lab-STICC/TOMS, IRISA/MYRIADS, IRISA/OBELIX) and one industrial member (CLS), will implement an applied research program with a TRL-4 target. The expected impacts of the project include both dissemination actions to the scientific community, including a maritime traffic surveillance benchmark suite, and technological transfers to CLS with respect to future national and international calls on operational systems and services for maritime traffic surveillance and high-resolution environment monitoring. Keywords: maritime traffic surveillance, AIS, high-resolution satellite imaging, large-scale data stream management and processing, big data, data mining, behaviour analysis and modeling, ship detection and recognition.