SES21- Hands-on Guided Training for Autonomous Sensing of Targets with ALL-INT Fusion Basics on a Planetary Scale Cloud Based Platform

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Originally Aired - Tuesday, April 26 2:00 PM - 3:00 PM

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Event Location

Location: Homestead 1


Event Information

Title: SES21- Hands-on Guided Training for Autonomous Sensing of Targets with ALL-INT Fusion Basics on a Planetary Scale Cloud Based Platform

Description:

Description: Based on the DOD/IC challenges to utilize multimodal, all intelligence (ALL-INT) data sets for advanced data fusion techniques to produce actionable information across the integrated battlespace this session will focus on demonstratable ways to develop and visualize targets. Session II will provide hands on training on a planetary scale platform which ingests, curates, models and analyzes data for actionable insights.

The session provides the attendee cloud-based internet access to multi-INT models using Python libraries and a powerful hyperscale cloud-native infrastructure with access to and overviews of:    

  • A rich image repository which meets requirements of nearly all potential use cases
  • Easy and highly scalable computing power for operational and large-scale model deployment
  • Availability of key derived layers and algorithms for analysis-ready workflows
  • Ability to create personal data catalogs, including client data with restrictions of access
  • Provide hands-off-the-wheel design-time and hyperscale runtime platform for data refinement and operation triggering.

 

The objective of this training is to demonstrate and guide the attendee through machine learning (ML)-based analytic approaches and computational methods for autonomous detection and tracking observed by Earth Observation satellites such as Multispectral Imaging (MSI) and Synthetic Aperture Radar (SAR) sensors as well as SATCOM data regardless of the attendees geospatial and/or GEOINT practitioner level.  

Body of Knowledge Topics: commercial satellite, airborne, sensors, data visualization, open-source geospatial data, statistics, image segmentation, machine learning, computer vision, object, and target detections, cross-functional: synthesis.

 

Learning Outcomes for Attendee: This session/class is designed to demonstrate GEOINT career pathways regardless of the attendees’ current discipline and stage in academic training or career standing:

  • An understanding a global scale architecture
  • An understanding of the analysis trade-offs in building models for global-scale analysis
  • Calibrated expectations of what is possible with modern tools, and what remains difficult
  • Explore the use of basic tools for data exploration and machine learning on the planetary scale architecture.

*Note: Please bring your laptop! Documentation and training materials will be provided!

To sign up for this training session, click on the link below to access the registration portal:

https://usgif.org/symposium-training/

 

Type: Training Session


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