Dynamically Typed

Stanford OGNet

Researchers from Andrew Ng’s Stanford ML Group have “developed and deployed a deep learning model called OGNet to detect oil and gas infrastructure in aerial imagery.” They’ve used it to create an open dataset with 7,000+ images of nearly 150 oil refineries — including several facilities that were not yet included in existing public datasets — which they hope will make it easier to attribute satellite-detected methane emissions to their sources on the ground. The paper by Sheng & Irving et al. (2020) will be published at the 2020 NeurIPS workshop on Tackling Climate Change with Machine Learning. Research like this has the potential to become a key tool for climate policy makers and enforcers.