Crop Type Map

Crop type mapping uses satellite and street-level imagery to create detailed maps showing the types of crops grown in different areas. Current research focuses on leveraging deep learning models, including variations of HRNet and other convolutional neural networks, often combined with techniques like contrastive learning and self-attention mechanisms, to improve accuracy and efficiency. These maps are crucial for precision agriculture, enabling optimized resource management, improved yield predictions, and more effective policy-making, particularly in data-scarce regions. The development of robust and accessible crop type maps is significantly impacting food security and sustainable agricultural practices globally.