Abstract-Detection of surface water in natural environment via multi-spectral imagery has been widely utilized in many fields, such land cover identification. However, due to the similarity of the spectra of water bodies, built-up areas, approaches based on high-resolution satellites sometimes confuse these features. A popular direction to detect water is spectral index, often requiring the ground truth to find appropriate thresholds manually. As for traditional machine learning methods, they identify water merely via differences of spectra of various land covers, without taking specific properties of spectral reflection into account. In this paper, we propose an automatic approach to detect water bodies based on Dempster-Shafer theory, combining supervised learning with specific property of water in spectral band in a fully unsupervised context. The benefits of our approach are twofold. On the one hand, it performs well in mapping principle water bodies, including little streams and branches. On the other hand, it labels all objects usually confused with water as 'ignorance', including half-dry watery areas, built-up areas and semi-transparent clouds and shadows. 'Ignorance' indicates not only limitations of the spectral properties of water and supervised learning itself but insufficiency of information from multi-spectral bands as well, providing valuable information for further land cover classification.

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Conventional supervised classification approaches have significant limitations in the land cover classification from remote sensing data because a large amount of high quality labeled samples are difficult to guarantee. To overcome this limitation, combination with unsupervised approach is considered as one promising candidate. In this paper, we propose a novel framework to achieve the combination through object association based on Dempster-Shafer theory. Inspired by object association, the framework can label the unsupervised clusters according to the supervised classes even though they have different numbers. The proposed framework has been tested on the different combinations of commonly used supervised and unsupervised methods. Compared with the supervise methods, our proposed framework can furthest enhance the overall accuracy approximately by 8.2%. The experiment results proved that our proposed framework has achieved twofold performance gain: better performance on the insufficient training data case and the possibility to apply on a large area. Index Terms-Object association, land cover classification, combination of supervised learning and unsupervised learning.

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Monitoring of meteorological or/and oceanographic conditions is done on many Oil & Gas platforms offshore West and Central Africa (from Nigeria to Angola), but it is often only used in real-time and not necessarily archived on a hard-drive, or it is protected by each company’s IT firewalls thus making it difficult to send the information to the “outer world”. In 2010, TOTAL Oil & Gas Operator launched a project to give remote and public access to this real-time wind, current and also wave or other meteorological / oceanographic (“metocean”) data. The objectives of this initiative were multiple: • Improve weather and ocean hindcasts and forecasts, which will be beneficial to all Oil & Gas operations in Africa, • Help feed a database for future O&G developments; • Enable design checks after ∼1 year of operation; • Serve as a “black box” in case of an incident which could be due to environment; • Help feed or validate ocean and oil spill drift forecast in case of emergency; • Contribute to the international effort of monitoring the oceans in the long term (operational oceanography, climate change, etc.); • Encourage capacity building in Africa by supporting development and maintenance of technical solutions to reach objectives In 2013, with the support of the French Meteorological Office Météo-France, the data from half a dozen platforms offshore Nigeria, Congo and Angola will be available on the World Meteorological Organization’s (WMO) Global Telecommunication System (GTS). This paper will present the type of metocean stations that are part of this network “MODANET”, the IT architecture that was selected to send it out of the Company’s network, the quality control undertaken by Meteo France before sending it to the GTS, and future possible use of the data that are envisaged.