Intelligent analysis of Earth remote sensing data on the distribution of phytoplankton and pollutants in coastal systems

Currently, one of the topical areas of application of artificial intelligence methods in ensuring environmental monitoring of water resources is the analysis of Earth remote sensing images in order to control and prevent potentially dangerous changes in the environment. In the future, algorithms with elements of artificial intelligence form the basis of forecasting and decision-making systems. Systems for ensuring high-quality environmental monitoring can be improved using artificial intelligence methods, in particular, the development and application of special algorithms to prevent emergencies. The aim of the study is to develop an algorithm using artificial intelligence to detect spots of substances of various origins on the water surface. It has been established that the YOLOv4 convolutional neural network is applicable for high-quality detection of oil spots and bloom spots of phytoplankton populations. The developed algorithm was tested on real satellite images and showed an accuracy of 84-94%