Paper 13138-501
Paper 13138-501
Sense making from multi-source, electro-optical, remote sensing constellations (Plenary Presentation)
20 August 2024 • 3:35 PM - 4:15 PM PDT | Conv. Ctr. Room 6A
Abstract
With 140+ petabytes of historical data holdings, 3.8 million square kilometers of daily multi-spectral collection, integration of Synthetic Aperture Radar and newly launching assets every quarter, the opportunities to develop insight from sense making technologies at Maxar are ever growing. During this discussion, we will cover the challenges of collecting, organizing, and exploiting multi source electro-optical remote sensing systems at scale using modern machine learning architectures and techniques to derive actionable insights.
Presenter
Manuel Gonzalez-Rivero
Maxar Technologies (United States)
Manuel Gonzalez-Rivero has spent a career exploring remote sensing, machine learning, data science and computer vision on orbital platforms. An Alum of Carnegie Mellon University he worked at General Dynamics and Lockheed Martin building real time satellite payloads, working on remote sensing at ARL-PSU, building global machine learning pipelines at Planet Labs, Orbital Insight, and Maxar Technologies. As the Sr. Director of Applied Machine Learning at Maxar, Manuel leads teams that create the production platform for analytics and the core sense making function that delivers actionable insights to customers at scale.