Description: This lecture explores the transformative role of Deep Learning in Remote Sensing for agriculture, focusing on the challenges posed by limited ground truth data. These challenges are especially critical for applications like crop monitoring, yield prediction, and environmental sustainability, where data scarcity can limit model performance. We will delve into the small data problem, its impact on model generalisability, and introduce various cutting-edge Deep Learning techniques that can overcome these challenges. These include transfer learning, self-supervised learning, semi-supervised learning, few-shot learning, zero-shot learning, active learning, weakly supervised learning, multitask learning, process-aware learning, and ensemble learning. Additionally, we will cover the use of spatial k-fold cross-validation as a robust model evaluation technique in agricultural settings.
Duration: 90 minutes + 45-60 minutes
Lecture Flow (content):
Outcomes: By the end of this lecture, participants will have a solid understanding of the small data problem in Remote Sensing for agriculture and will be equipped with the skills to apply emerging Deep Learning techniques, such as transfer learning, self-supervised learning, data augmentation, and spatial k-fold cross-validation. They will be able to improve model performance in data-limited environments, particularly for agricultural applications like crop classification, and will be capable of evaluating and enhancing model generalisability using these techniques.