Deep Learning for Sentinel-1 and Sentinel-2 Based Crop and Land Cover Mapping

This collection explores the use of deep learning techniques applied to Sentinel-1 and Sentinel-2 data for precise crop classification and land cover mapping. The studies focus on leveraging advanced neural networks to enhance the accuracy of monitoring agricultural landscapes and land use. By combining SAR and multispectral data, these approaches offer significant improvements in the detection and mapping of crops and land features. This collection provides valuable insights for researchers, remote sensing professionals, and students working in the fields of agriculture, land management, and environmental monitoring.