Transforming GEOINT Processing, Exploitation & Dissemination Structured Observation … · goal:...

© 2019 BAE Systems. All Rights Reserved. ClearFlite, GXP, GXP OpsView, GXP WebView, GXP Xplorer, SOCET GXP, and SOCET SET are registered trademarks of BAE Systems. This document gives only a general description of the product(s) or service(s) offered by BAE Systems. From time to time, changes may be made in the products or conditions of supply. The commodities are controlled under the Export Administration Regulations (EAR). E-20190317-74. NOT INTENDED FOR PUBLIC RELEASE.

Presented by Janis McArthurProduct Development Manager, Analytics

Transforming GEOINT Processing, Exploitation & DisseminationStructured Observation Management (SOM) Machine Learning & Analytics

GXP360º Professional Exchange and WorkshopMarch 18-22, 2019 | San Diego, California

© 2019 BAE Systems. All Rights Reserved. E-20190317-74. NOT INTENDED FOR PUBLIC RELEASE. SOM & Machine Learning | Janis McArthur 2

SOCET GXP

DESKTOP

GXP WebView

WEB-BASED

GXP InMotion GXP OpsView GXP Multi-INT

Platform

GXP Xplorer GXP Xplorer Disconnected

COMMON USER INTERFACE

DevLab/Partners

GXP Product Ecosystem Goal 2020Provide the premier intelligence exploitation toolset to drive GEOINT transformation


© 2019 BAE Systems. All Rights Reserved. E-20190317-74. NOT INTENDED FOR PUBLIC RELEASE. SOM & Machine Learning | Janis McArthur 3

DESKTOP WEB-BASED

Platform

COMMON USER INTERFACE

SOM and Machine Learning in all Products in GXP Product Ecosystem Provide the premier intelligence exploitation toolset to drive GEOINT transformation

GXP Xplorer DisconnectedDevLab/Partners GXP Xplorer

SOCET GXP GXP InMotion GXP OpsView GXP Multi-INTGXP WebView


© 2019 BAE Systems. All Rights Reserved. E-20190317-74. NOT INTENDED FOR PUBLIC RELEASE. SOM & Machine Learning | Janis McArthur

``

4

WHAT ARESTRUCTURED OBSERVATIONS?



``

5

ARE WHAT ANALYSTS ALREADY DOSTRUCTURED OBSERVATIONS



GXP® goal: make observation collection and analysis more efficient with automation

6

• Transform processing, exploitation, and dissemination (PED) workflows to a future state

• Exploit all data • Use machine to find objects• Find even mundane and non-mission objects

• Structure observations for machine use • Add structure to unstructured • 4-dimensional, time of observation

• Aggregate and mine observations• Connect to external observation stores• Provide statistics and geo-analytics• Use machine to find patterns in data too

voluminous for human comprehension• Help analyst predict possible actions

Field:: xyzField: abcdwfField:ghiField:jklmnop qrstField: 12345qwert

Summary: This could be a few sentences describing what occurred and where and over what time period and with what entities people equipment and the sequence of events and what the end result was



Products / Reports



Spreadsheets

The Past The Future

Predict Possible ActionsAutomation

& MachineLearning

Source Data

Automation & MachineLearning

Multi-Source Multi-INT Observations

ObservationsBig Data



How GXP is transforming workflows with automation

7

Manual Observation and Product Generation

Activity Reporting Tool for Order of Battle monitoring

Observations from machine object detection imported for analyst to verify and reinforce machine learning and stored as data services

Observations recorded by analyst looking at imagery are stored in product files

Observations structured with automatic attribution and stored as data services

Machine Learning Enabled Workflows

Machine Impact Invisible to User

Analytics from Observations

Machine analytics tools operate on Big Data of observations to help analyst understand activities and make predictive hypotheses

Automation incorporated with minimal disruption to analysts’ intelligence tasks



Current solutions for observation management in GXP® products

8

Activity Reporting Tool (ART)

US SOM

Visualization

Storage

Collection

PostGISArcGIS® GXP Xplorer®



Future solutions for observation management with GXP products

9

Visualization

Storage

Collection

GETSTEDPostGISArcGIS GXP Xplorer

GXP InMotion GXP Multi-INT

Machine Learning (ML) Engines

MAVENSPOTRIDenTVEtc.

External Observation Stores

Visual ProfilerDeepObjectVeritoneDataCapable

US SOMUNICORN

Etc.

GXP InMotion GXP Multi-INT



Automation technology: ART in SOCET GXP® today

10

• Repeatable workflow for first phase observations for monitoring activity changes

• Time savings: • Workflow wizard reduces re-

work due to missed steps• Previous data retrieval and

auto-population of metadata saves data entry time



Automation technology: ART in SOCET GXP – Next Generation with ML in 2019

11

• Blend three tasks:• Review imported ML

observations• Provide training data back

to ML for reinforcement learning to improve detection

• Create intelligence products

• Time savings: • View image one time• Reduce time to find and

count objects by using ML



Automation technology: GXP Xplorer Configurable Data Model

12

• Configure the structured metadata of objects that are of interest to your organization – land, air, maritime, persons, events

• Use your icons and naming conventions or “ontology” for object and observation attributes

• In future, configuration will map observations from external data stores to a normalized ontology for import, export, and federated analysis

• Time savings:• Streamline observation collection by focusing on

objects of interest• Retrieve from and run analytics from multiple data

stores at once by mapping to a normalized ontology



Automation technology: GXP Xplorer Places of Interest (POIs)

13

• POIs are locations and facilities

• Import POI boundaries and schemas

• Associate observations and activities to POI

• Time savings: • Select POI from picklist• POIs filtered by image or area boundary• Jump to POI on map by name• Filter observations by POI for analysis



Automation technology: GXP Xplorer Workflow Manager

14

• Create workflows for key tasks

• Associate the data needed

• Alert analyst or supervisor

• Tasks held in queues per person

• Time savings: • Automatic flow and alerts from person

to person reduce coordination time• Work package loads automatically no

searching for data

Tasks Created

Tasks Assigned / Queued

Tasked Worked

Products Created QA / QC Task

Completed

Task

Work Package

Image 1Image 2Image …

Completed Product(s) Once Task is Complete

Supplemental Files

Metadata About the Task



QA/QC POSSIBLE

PUBLISH PRODUCT

DO INTEL TASK

WEST AIRFIELD

388.7 m

14NOV21020728-P1BS-054994019020_01_P00128JAN16

Image Center Point: Lat: 37.4674 Lon: 126.4412Product Produced By: steven.carrilloMar 10, 2019

GXP 360 DEMO TEMPLATE

UL: 373130.2892N1262124.2625EUR: 373148.0660N1263225.9753ELR: 372415.9830N1263224.9591ELL: 372358.7274N1262126.8690E

IMAGE CENTER POINT: Lat: 37.4674 Lon: 126.4412

Automation technology: Observations captured invisibly

15

Intelligence observations



WEST AIRFIELD

388.7 m

14NOV21020728-P1BS-054994019020_01_P00128JAN16





Automation technology: Observations captured invisibly …2

16

388.7 m

14NOV21020728-P1BS-054994019020_01_P001





MENSURATION DATA

OBJECT IDENTIFCATION

PRODUCT METADATA



WEST AIRFIELD

388.7 m

14NOV21020728-P1BS-054994019020_01_P00128JAN16





388.7 m

14NOV21020728-P1BS-054994019020_01_P001





17


WEST AIRFIELD

TAGGING



WEST AIRFIELD

388.7 m

14NOV21020728-P1BS-054994019020_01_P00128JAN16





388.7 m

14NOV21020728-P1BS-054994019020_01_P001





18


WEST AIRFIELD

388.7 m

• User taggable attributes:• Object type• Object name• Object label (“Miller school”)• Comment

• Auto-populated attributes: • Geometry (polygon,line,point)• Image chip of observation• Provenance: image file name• Provenance: graphics file name• image date • Significant image metadata• Analyst ID• Lat/long/Z • line, sample • GUID of image in Xplorer• Security classification




19


WEST AIRFIELD

388.7 m

•User taggable attributes:•Object type•Object name•Object label (“Miller school”)•Comment

•Auto-populated attributes: •Geometry (polygon,line,point)•Image chip of observation•Provenance: image file name•Provenance: graphics file name• image date •Significant image metadata•Analyst ID•Lat/long/Z • line, sample •GUID of image in Xplorer•Security classification



GXP Machine Learning enabled workflows

• Pain Point : - Focus has been on sensors and collection- Analyst workflows have not adapted- Large data sets are ‘left behind’- AI/ML tool training is a further burden

• GXP’s Solution:- Integrates ML/AI into existing GXP workflows- Enables and orchestrates multiple algorithms- Looks at 100% of cataloged data- Automates generation of training data- Allows workflow owners to adopt at their pace

Goal: Bring ML into GEOINT exploitation



GXP Machine Learning enabled workflow

CURRENT ANALYST WORKFLOW

Active Mission Data Imagery/Video



GXP Machine Learning enabled workflow …2



GXP Xplorer Catalog And Structured Observations







SOCET GXP Human Expert Analysis




Workflow Manager








Workflow Manager


PRODUCTSVERIFIED OBSERVATIONS




Quality Check And Approval (QA/QC) Human Supervisors



GXP Machine Learning enabled workflow ….6

Workflow Manager

Products

APPROVED PRODUCTS










Workflow Manager

Products

APPROVED PRODUCTS


MACHINE ENABLED WORKFLOW

Machine Learning Orchestrator









Workflow Manager

Products

APPROVED PRODUCTS












Workflow Manager

Products

PRODUCTS

APPROVED PRODUCTS

VERIFIED OBSERVATIONS










GXP Machine Learning enabled workflow ….10

Workflow Manager

Products

PRODUCTS

APPROVED PRODUCTS















Workflow Manager


Products

PRODUCTS

APPROVED PRODUCTS











Workflow Manager


Products

PRODUCTS

APPROVED PRODUCTS




Machine Learning Orchestrator Active training learning engine







Workflow Manager


Products

PRODUCTS

APPROVED PRODUCTS




Machine Learning Orchestrator Active training learning engine ML training data repository







Workflow Manager


Products

PRODUCTS

APPROVED PRODUCTS




Machine Learning Orchestrator Active training learning engine ML training data repository



Demonstration: Upcoming Machine Learning enabled workflow in SOCET GXP

35



GXP architecture for Machine Learning: Protecting your ML investments

36

Manual Observation and Products

Activity Reporting Tool for Order of Battle monitoring

Machine Learning Enabled Workflows

Machine Impact Invisible to User

Analytics from Observations Architecture for ML

plug-ins and orchestration



GXP and Machine Learning: Architecting for advanced machine learning

37

• We are designing an architecture for future machine learning and analytics

• Easy to add in machine learning • Plug-and-play architecture for ease of addition of ML engines• No-hassle access to best of breed COTS and GOTS ML • Don’t get stuck on a point solution

• Ensemble Learning selects best answers from multiple solutions

Ensemble LearningAll engines run over same source

Highest Confidence per Detection

Ensemble LearningAll engines run over same source

Plug-in new ML engines



GXP and Machine Learning: Advanced machine learning orchestration

38

Orchestrated Learning of Object Detectors

To learn more please attend Workshop 1.3 SOM and Advanced

Analytics and visit Vertione exhibit at the GXP360

Orchestrated Learning of a variety of ML engine classes to build knowledge



Goal: Collect all 100 m or greater length ships in the image. Compare times between 3 SOCET GXP workflows.

1. Manual: Use Feature Extraction to manually make observations of vessels2. ART: Use ART to make semi-automatic observations of vessels3. Machine Learning (ML) Enabled: Review overlaid DeepObject observations of vessels, delete wrong ones and add

observations for any missed vessels using feature extraction

• ML (DeepObject) training time: used 15 iterations of training and took a week to build. • Trained ML runtime: 1 minute to run on image. 9 correct ships, 1 false positive, 1 false negative. WorldView image

GSD .311 m. Computer: Intel® Xeon® CPU E5-1620 [email protected] GPU: Quadro K6000, Tesla K40c

Experiment: Man vs. Machine

39

Analyst Workflow Time Success percent1 Manual 4 m 25 s 602 ART 3 m 7 s 1003 ART 2 m 56 s 70

Average 1&2 ART 3 m 2 s 854 ML-Enabled 1 m 20 s 100

mailto:[email protected]



• Image analyst times from Boot Camp challenge

• Time savings or more images that could be processed per year

Experiment: Man vs. Machine …2

40

Workflow Average Time Success percentManual 4 m 25 s 60

ART 3 m 2 s 85ML-Enabled 1 m 20 s 100

Workflow Annual ime Time savingsManual 4 m 25 s 60

ART 3 m 2 s 85ML-Enabled 1 m 20 s 100



Imagery and data providers

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• clipartmax.com

• Maxim Basinski from flaticon.com licensed by CC 3.0 BY

• Freepikis licensed by CC 3.0 BY

• shareicon.net

• iconfinder.com

• DigitalGlobe

• Veritone

• BAE Systems GP&S

• acclaimimages.com

• ngtree.com

• chittagongit.com

• onlinewebfonts.com

BAE Systems would like to thank the following organizations for providing data for use throughout this presentation:



We value your feedback

42

We improve because of your feedback!

Please take a moment to complete the brief paper survey that was provided to you at the beginning of this session.

If you did not get a survey, see your session monitor in the back of the room or your session instructor.

We appreciate your time and suggestions!

© 2019 BAE Systems. All Rights Reserved. ClearFlite, GXP, GXP OpsView, GXP WebView, GXP Xplorer, SOCET GXP, and SOCET SET are registered trademarks of BAE Systems. This document gives only a general description of the product(s) or service(s) offered by BAE Systems. From time to time, changes may be made in the products or conditions of supply. The commodities are controlled under the Export Administration Regulations (EAR). E-20190317-74. NOT INTENDED FOR PUBLIC RELEASE.

Janis McArthurTel: 619 507 3468E-mail: [email protected]

Thank you!

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