- By:
- Reith, Andrew EJ; McKee, Jacob J; Rose, Amy N; Laverdiere, Melanie L; Swan, Benjamin T; Hughes, David C; Voisin, Sophie ; Yang, Hsiuhan ; Varma, Laurie W; Neunsinger, Elizabeth L; Lunga, Wadzanai D
- Page Number:
- 153-168
- Book Title:
- Advances in Scalable and Intelligent Geospatial Analytics: Challenges and Applications
- Publication Date:
- November 9, 2023
- Publisher Location:
- CRC Press, Boca Raton, Florida, United States of America
Abstract
This chapter describes ORNL’s (Oak Ridge National Laboratory’s) contributions to imagery preprocessing for geospatial intelligence research and development (R&D) in four sections. First, we discuss challenges involved in building an effective imagery preprocessing workflow and the world-class high-performance computing (HPC) resources at ORNL available to process petabytes of imagery data. Second, we highlight how we developed imagery preprocessing tools over three decades while paving the way for our current cutting-edge machine learning and computer vision algorithms that are impacting humanitarian and disaster response efforts. Third, we discuss how PIPE modules work together to turn raw images into analysis-ready datasets. Fourth, we look toward the future and discuss planned advancements to PIPE and computing trends that will affect geospatial intelligence R&D.
