Deploying a Self-Supervised Learning Based Model to Search Events Across Space and Time

Abstract

Motivation - Scientific Study of natural events, phenomena, or disasters require examples which span across time and space. - Machine Learning adaptation is on the rise, but there’s a lack of labeled training datasets that could be used to train or validate the models. - Best case scenario: - There’s an event database that tracks events available through time and space. - Provides all data associated with the events. - Real life scenario: - Some events are better tracked than others. - Scientists need to spend significant time identifying and gathering examples of events from different sources.