Deep Learning Model Training and Validation Data for Global Floating Algae Detection
Published: 14 August 2025| Version 1 | DOI: 10.17632/f39zt9g2c4.1
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Description
This dataset contains the training and validation data used to develop and evaluate a ResUNet deep learning (DL) segmentation model for detecting floating algae from MODIS/Aqua imagery at the global scale. The model was trained using inputs including MODIS Rayleigh corrected reflectance (Rrc) in 7 spectral bands and the Alternative Floating Algae Index (AFAI), and is capable of identifying both microalgae (phytoplankton) scums and macroalgae (seaweed) mats. These include Trichodesmium, Noctiluca, Dinoflagellates, Cyanobacteria, Sargassum, and Ulva.
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Institutions
NOAA Office of Satellite Operations, University of South Florida
Categories
Oceanography, Remote Sensing, Harmful Algal Blooms