Kate Werling

SpaceNet, a nonprofit LLC focusing on solving geospatial problems such as mapping road network routes after a natural disaster, has open-sourced more than 6,500 square kilometers of high-resolution satellite imagery with approximately 800,000 building footprint labels and 8,000 square kilometers of road network labels. Join us for a discussion on how to use this data to train machine learning...

The first hour after a natural disaster is often referred to as the “golden hour," when responders have the highest chance of saving the lives of those effected by the disaster—but it's also when they have least amount of information available to them. In this chalk talk, we walk through different ways machine learning can help accelerate a responder’s understanding of the areas impacted and...

In this chalk talk, learn how to get started building, training, and deploying your first machine learning model. We then discuss tips, tricks, and best practices on starting your machine learning journey.