Title: Weakly Supervised Learning for Satellite Imagery: Applications in Crop Mapping
Speaker: Sherrie Wang
Feeding 7 billion people -- a number likely to surpass 9 billion by 2050 -- will require smarter agriculture. Knowing where crops grow worldwide is a crucial first step. Today, this information is acquired through on-the-ground surveys, which take a long time, require many people, and are tough to conduct in the countries where data is needed most. Tomorrow, it will be possible to harness satellite imagery and machine learning to decrease the cost and difficulty of mapping this information at scale. However, one main challenge in applying the tools of machine learning to crop type mapping is the low quantity of ground truth labels on which to train state-of-the-art methods (e.g. deep learning). This talk will offer a window into how unsupervised and weakly supervised learning methods can help us bridge this label gap and understand which crops are grown where.
Sherrie is a 4th year PhD student at Stanford’s Institute for Computational and Mathematical Engineering (ICME), advised by Professor David Lobell at the Center on Food Security and the Environment. Her research focuses on developing semi-supervised and unsupervised methods for remote sensing data to enable understanding of food systems and their interaction with the environment at a large scale.