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Deep learning tasks are often complicated and require a variety of componentsworking together efficiently to perform well. Due to the often large scale ofthese tasks, there is a necessity to iterate quickly in order to attempt avariety of methods and to find and fix bugs. While participating in IARPA'sFunctional Map of the World challenge, we identified challenges along theentire deep learning pipeline and found various solutions to these challenges.In this paper, we present the performance, engineering, and deep learningconsiderations with processing and modeling data, as well as underlyinginfrastructure considerations that support large-scale deep learning tasks. Wealso discuss insights and observations with regard to satellite imagery anddeep learning for image classification.

Focusing on the Big Picture: Insights into a Systems Approach to Deep Learning for Satellite Imagery