Self-supervised Contrastive Learning for Irrigation Detection in Satellite Imagery
This addresses the challenge of monitoring agricultural water usage for sustainable food security, particularly in areas like developing countries where labeled data is scarce.
The paper tackled the problem of automatically detecting irrigation in satellite imagery without requiring expensive labeled data, achieving up to nine times better precision, 90% better recall, and 40% more generalization ability compared to traditional supervised methods.
Climate change has caused reductions in river runoffs and aquifer recharge resulting in an increasingly unsustainable crop water demand from reduced freshwater availability. Achieving food security while deploying water in a sustainable manner will continue to be a major challenge necessitating careful monitoring and tracking of agricultural water usage. Historically, monitoring water usage has been a slow and expensive manual process with many imperfections and abuses. Ma-chine learning and remote sensing developments have increased the ability to automatically monitor irrigation patterns, but existing techniques often require curated and labelled irrigation data, which are expensive and time consuming to obtain and may not exist for impactful areas such as developing countries. In this paper, we explore an end-to-end real world application of irrigation detection with uncurated and unlabeled satellite imagery. We apply state-of-the-art self-supervised deep learning techniques to optical remote sensing data, and find that we are able to detect irrigation with up to nine times better precision, 90% better recall and 40% more generalization ability than the traditional supervised learning methods.