NEURAL NETWORK METHODS FOR PROCESSING SATELLITE IMAGERY FOR GEO-ENVIRONMENTAL MONITORING AND NATURAL HAZARD ASSESSMENT
Keywords:
neural networks, satellite imagery, geo-environmental monitoring, natural hazards, deep learning, landslides, floodsAbstract
The paper provides a review of modern neural network methods for processing satellite imagery applied to geo-environmental monitoring and natural hazard assessment. Deep learning architectures are considered, including convolutional neural networks, segmentation models (U-Net, DeepLab), recurrent networks, and transformers. Particular attention is paid to the application of these methods for landslide mapping, soil erosion assessment, flood monitoring, and water quality analysis. International experience is analyzed, and the prospects for applying neural network technologies in the mountainous regions of Kyrgyzstan are highlighted. The advantages and limitations of the approaches are outlined, and directions for further research are suggested in the context of developing a national system for environmental monitoring and disaster risk reduction.
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