首页|Study Findings from University of Southern Florida Broaden Understanding of Arti ficial Intelligence (Detection of Karenia Brevis Red Tides On the West Florida S helf Using Viirs Observations: Accounting for Spatial Coherence With Artificial ...)
Study Findings from University of Southern Florida Broaden Understanding of Arti ficial Intelligence (Detection of Karenia Brevis Red Tides On the West Florida S helf Using Viirs Observations: Accounting for Spatial Coherence With Artificial ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Artificial Intelligence. According to news reporting originating in St. Petersbu rg,Florida,by NewsRx journalists,research stated,"Harmful algal blooms (HABs ) of the toxic dinoflagellate Karenia brevis (K. brevis) occur annually on the W est Florida Shelf (WFS). Detection of these blooms using satellite observations often suffers from two problems: lack of accurate algorithms to identify phytopl ankton blooms in optically complex waters and patchiness (i.e.,heterogeneity) o f K. brevis during blooms." The news reporters obtained a quote from the research from the University of Sou thern Florida,"Here,using data collected by the Visible Infrared Imaging Radio meter Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP) betw een 2017 and 2019,we develop a practical approach to overcome these difficultie s despite the lack of a chlorophyll-a fluorescence band on VIIRS. The approach i s based on artificial intelligence (specifically,a deep-learning (DL) convoluti onal neural network model),which uses spatial coherence of bloom patches to acc ount for the patchiness of K. brevis concentrations. After proper training,the overall performance (i.e.,F1 score) of the deep learning model is 89% . Extracted K. brevis patches were consistent with those derived from the Modera te Resolution Imaging Spectroradiometer (MODIS) on the Aqua satellite,which has a fluorescence band. Furthermore,the wider swath of VIIRS over MODIS (3040-km versus 2330-km) led to more valid observations of bloom extent,enabling improve d near-realtime applications."
St. PetersburgFloridaUnited StatesNorth and Central AmericaArtificial IntelligenceEmerging TechnologiesMachi ne LearningUniversity of Southern Florida
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