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Proceedings Paper

Data fusion for sociocultural place understanding using deep learning
Author(s): Jake Popham; Micheal Forkin; Nicholas Hamblet; Bryce Inouye
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Paper 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).

Paper Details

Date Published: 27 April 2018
PDF: 18 pages
Proc. SPIE 10653, Next-Generation Analyst VI, 106530E (27 April 2018); doi: 10.1117/12.2306881
Show Author Affiliations
Jake Popham, Commonwealth Computer Research, Inc. (United States)
Micheal Forkin, Commonwealth Computer Research, Inc. (United States)
Nicholas Hamblet, Commonwealth Computer Research, Inc. (United States)
Bryce Inouye, Commonwealth Computer Research, Inc. (United States)

Published in SPIE Proceedings Vol. 10653:
Next-Generation Analyst VI
Timothy P. Hanratty; James Llinas, Editor(s)

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