Satellite imagery is an invaluable source of information for a wide range of industries, from agriculture to urban planning to disaster response. However, processing and analyzing this data can be a daunting task, particularly as the amount of available data continues to grow. In this talk, we will explore how artificial intelligence (AI) can be used to process satellite imagery at scale in the cloud. We will begin by discussing the challenges of processing large volumes of satellite imagery, including issues related to storage, processing power, and data quality. We will then explore how AI techniques such as machine learning and computer vision can be used to automate the process of analyzing this data, including identifying features such as roads, buildings, vehicles, and vegetation. Next, we will discuss the benefits of using cloud computing platforms such as Amazon Web Services (AWS) for processing satellite imagery. These platforms offer powerful tools for managing and processing large volumes of data, as well as advanced AI services such as SageMaker to enable computer vision solutions. Finally, we will explore a case study of how AWS developed a novel approach to help customers leverage the cloud to process large volumes of satellite imagery against computer vision models. We will discuss the strategy, architecture, and lessons learned from this project, as well as the potential for future innovations to meet the needs of this rapidly evolving field.
Overall, this talk will provide attendees with a comprehensive overview of how AI and cloud computing can be used to process satellite imagery at scale, and will highlight the benefits and challenges of this approach. Attendees will leave with a deeper understanding of the tools and techniques available for processing satellite imagery.