Providing Geospatial Intelligence through a Scalable Imagery Pipeline

Book · Mon May 01 00:00:00 EDT 2023

DOI:https://doi.org/10.1201/9781003270928-11· OSTI ID:1976065

Reith, Andrew ^[1]; McKee, Jacob ^[1]; Rose, Amy ^[1]; Laverdiere, Melanie ^[1]; Swan, Benjamin ^[1]; Hughes, David ^[1]; Voisin, Sophie ^[1]; Yang, Lexie ^[1]; Varma, Laurie ^[1]; Neunsinger, Liz ^[1]; Lunga, Dalton ^[1]

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1. ORNL

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1976065
• Country of Publication: United States
• Language: English