Abstract
Monitoring the extension of irrigated areas at different stages of an agricultural cycle is of great interest for land and water resources management.With a limited training dataset (50 three bands Landsat 8 images), we rely on a transfer learning architecture combining UNet and ResNet50 as backbone to intelligently map irrigated areas.Three optimization methods are evaluated: Adam and two variants of Stochastic Gradient Descent (SGD) associated with two techniques (Cyclical Learning Rate and Warm Restart).We also assess the impact of data augmentation and temporal generalization of the model learned by predicting the location of irrigated areas at specific periods of the same agricultural cycle.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.