Data fusion for sociocultural place understanding using deep learning

Jake Popham; Micheal Forkin; Nicholas Hamblet; Bryce Inouye

doi:10.1117/12.2306881

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27 April 2018 Data fusion for sociocultural place understanding using deep learning

Jake Popham, Micheal Forkin, Nicholas Hamblet, Bryce Inouye

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Proceedings Volume 10653, Next-Generation Analyst VI; 106530E (2018) https://doi.org/10.1117/12.2306881
Event: SPIE Defense + Security, 2018, Orlando, Florida, United States

Abstract

To be effective in complex operations, the U.S. military requires understanding about populations in the physical and information environments. Operations executed without sufficient understanding lead to unintended consequences with potentially far-reaching implications. We present Apropos, a platform that aims to improve mission outcomes through socioculturally informed course of action analyses. Apropos uses deep learning over multiple data modalities to efficiently derive information on operational and civil factors. Research efforts focus on deep learning approaches and model fusion techniques centered around knowledge graph embeddings enabling semantic search, predictive surfaces, and other analytics (e.g. route planning and site selection).

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Jake Popham, Micheal Forkin, Nicholas Hamblet, and Bryce Inouye "Data fusion for sociocultural place understanding using deep learning", Proc. SPIE 10653, Next-Generation Analyst VI, 106530E (27 April 2018); https://doi.org/10.1117/12.2306881

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