Descripción del proyecto
Rediseñar las estrategias de limpieza de vertidos de petróleo
Los vertidos de petróleo catastróficos son imprevisibles y constituyen una grave amenaza para el medio ambiente y la economía. Responder con rapidez y eficacia ante ellos es fundamental. El éxito de la respuesta a los vertidos de petróleo reside en la detección precoz, la contención y la limpieza eficaz. En el proyecto SOSeas, que cuenta con el apoyo de las acciones Marie Skłodowska-Curie, se emplearán radares de apertura sintética (SAR, por sus siglas en inglés) de última generación e inteligencia artificial (IA) para detectar el espesor relativo del petróleo. Esta innovación solventa el problema de la subjetividad de los muestreos aéreos tradicionales para, así, ofrecer una evaluación precisa. La tecnología SAR transforma la vigilancia de los vertidos de petróleo, ya que puede obtener imágenes de día y de noche en cualquier condición meteorológica. El equipo de SOSeas empleará datos SAR gratuitos y algoritmos de IA para proporcionar información esencial para la respuesta a los vertidos de petróleo.
Objetivo
"Oil spills rapidly spread on sea surfaces covering wide areas, assuming different appearances and thicknesses. The faster the actions to detect, stop, and contain the released oil from spreading, the higher the Oil Spill Response (OSR) success rate. Since, clean-up effectiveness is higher over thicker layers of oil - referred to as actionable oil - detecting these regions is crucial to enhance oil recovery efficiency, thus minimizing environmental and socio-economic impacts. The objective of ""Searching for Oil Spills on Sea Surfaces"" (SOSeas) project is to develop an artificial intelligence-based system to extract relative oil thicknesses by using multifrequency and multiresolution Synthetic Aperture Radars (SAR). Aerial reconnaissance is currently the most common method to estimate the extent, thickness, and volume of oil spills. However, it is subjective, biased and imprecise, demanding well-trained experts to visually estimate the extent of an oil slick and distinguish different oil appearances. Conversely, SAR are key-operational sensors for oil pollution monitoring, offering a synoptic view over affected sites, acquiring images during day and night regardless of weather conditions. The use of SAR data to detect the location and extent of oil pollution, as well as to discriminate it from false alarms has been well-researched. However, oil slicks characterization is under-explored, but a promising, new, and highly innovative research area, owing to the increasing availability of free SAR data, the development of powerful learning algorithms combined with high-performance computing advances. An automatic system well-trained to recognize patterns related to qualitative thickness ranging will indicate the actionable oil regions. These outputs can offer a less subjective and more precise oil pollution assessment than that of visual reconnaissance, improving situational awareness in time to guide trustworthy decision-making during clean-up operations.
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Ámbito científico
- engineering and technologyelectrical engineering, electronic engineering, information engineeringinformation engineeringtelecommunicationsradio technologyradar
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensors
- natural sciencesearth and related environmental sciencesenvironmental sciencespollution
Palabras clave
Programa(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régimen de financiación
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinador
7522 NB Enschede
Países Bajos