Data fusion for sociocultural place understanding using deep learning

Jake Popham; Micheal Forkin; Nicholas Hamblet; Bryce Inouye

doi:10.1117/12.2306881

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

Jake Popham, Micheal Forkin, Nicholas Hamblet, Bryce Inouye

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

Conference Presentation

Citation Download Citation

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

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available