Remote sensing and computer vision algorithms at scale: defense and humanitarian uses

The objective of this thesis is to understand how cloud computing and artificial intelligence can be applied to vast amounts of remotely sensed data to better understand macro-level trends for humanitarian and defense issues. Computer vision algorithms and data were provided by Orbital Insight, Inc., a geospatial analytics company based in Palo Alto, CA. Specific projects were curated, data was acquired, and analysis was applied to three use cases: "Patterns of life for The Battle of Marawi", "Indications and Warnings using multi-class aircraft detections", and "Camp Fire land cover analysis". The use cases show how with imagery ingestion pipelines, cloud computing, and computer vision algorithms, a massive quantity of data can be analyzed in a relatively short amount of time. Without these workflows and new technologies, analysis of large amounts of data would prove to be less efficient and resource heavy. The events show the benefits users of spatial data would have to gain a better understanding of humanitarian and defense issues. Algorithms used to derive insights from thousands of imagery scenes consisted of a car detection algorithm, multi-class aircraft algorithm, and a land cover classification algorithm. Additionally, the thesis briefly explores the use of geolocation data to supplement computer-vision algorithm data. The thesis shows on a high level, through examples, how users could use the technologies to analyze data more efficiently. This analysis can be incorporated into high-level humanitarian or defense decisions. Future work regarding this field should seek to evaluate the algorithm performance on a more granular level. Researchers should also build different algorithms on open-source imagery to allow for more users to benefit from the efficiency computer vision provides.