A hybrid deep learning model based on vision transformer and convolutional neural networks for land use and land cover classification

Key Points

• The hybrid model improves land cover classification accuracy, addressing existing method limitations.
• Performance metrics illustrate a notable accuracy increase of 15% compared to conventional models.
• Analysis utilized convolutional neural networks alongside vision transformer components, optimizing data processing.
• Implications include better precision in environmental assessments, enhancing policy decisions for land management.