A Fusion model for the accurate Above-Ground Biomass (AGB) estimation from Satellite Images using GAN-CNN

Key Points

• The central aim is to develop a fusion model combining GAN and CNN for precise estimation of above-ground biomass.
• Developed a machine learning model using Generative Adversarial Networks (GAN) and Convolutional Neural Networks (CNN).
• Applied the model to satellite images to assess above-ground biomass.
• Conducted validation tests to evaluate estimation accuracy.
• Achieved significant improvements in biomass estimation accuracy compared to traditional methods, with a reported accuracy of X% (actual figure not specified).
• Demonstrated the model's ability to generalize across different ecological settings, enhancing ecological monitoring capabilities.