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Interoperable Simulation and Gaming Mesh

Description:

TECHNOLOGY AREA(S): Human Systems, Information Systems, Sensors, Battlespace

OBJECTIVE:

This topic seeks to demonstrate automated interoperability of simulation and gaming by taking tactical sensor data collected as gaming mesh that can be correctly georeferenced to the earth's surface and transforming it into Open Geospatial Consortium (OGC) CDB data segmented into appropriate data layers.

DESCRIPTION:

SOCOM provides Special Operation Forces (SOF) with operational intelligence that enables joint SOF mission planning and rehearsal for real-world combat environments. Current processes, mostly manual, leverage source data including imagery of varying types and resolutions, vector data, and elevation data to produce three-dimensional (3D) scene visualization databases and enhanced Geospatial Intelligence (GEOINT) data such as maps, imagery, and terrain models. 3D databases support battlespace visualization and simulation so that SOF units know the areas where they will operate in before they get there. This SBIR topic will investigate automated processes to accelerate production of OGC CDB data stores using sensor data source collected from small tactical UAS in meshed terrain format not traditionally associated with geographic information systems or Defense modeling and simulation.


The solution needs to recognize sensor data as points, imagery raster and/or meshed data and produce the appropriate OGC CDB layers. Most of the tactically collected data has some geo-referencing data to get it close to where the data exists in the real world and the data has good relative accuracy. If the data can be edge matched via pattern recognition to existing imagery to transform it into the correct place on the earth surface, it will improve the geospatial accuracy of the source data. Once the data is in the right location then the data needs to be segmented to provide a good Digital Terrain Model or Digital Elevation Model, and the rest of the 3D features extracted into OGC CDB models. Potential solutions may use OGC CDB raster material data and/or multi- or hyper-spectral imagery signatures to improve segmentation and then apply those material codes to the polygonal surfaces to improve the data for simulation ready applications like Unity and Semi-Automated Forces support. Artificial intelligence and/or machine learning algorithms be used to train and then invoke these procedures, reducing the need for manual intervention to pick tie points between the imagery and the vector data after enough tie points are established to transform the vector data to the imagery to correlate the data. Solutions should learn and, given a set of data, be able to recognize patterns in the data to automatically tie the vectors to the imagery.


High-level goals include:

  1. Reduce (T)/eliminate (O) manual intervention necessary to build CDB data layers.
  2. Minimal training (T) / no expert knowledge (O) required for basic use.
  3. Customization through a drag-and-drop workflow creation/editing tool (O).
  4. Implementation of AI/ML techniques to provide for a guided training mode that can be used to improve or customize autonomous processing outcomes (O) (ex: correlation of vector data with underlying imagery).
  5. Ability for user to manually identify sets of source data for processing (T/O), including standardized OGC web services (O).
  6. Ability to monitor a Watch Folder for input data (T/O).
  7. Ability to accept and recursively follow links in the Watch Folder and defined data stores (T/O).
  8. Execute autonomous actions and CDB creation workflows when presented with appropriate geospatial input data (T/O).
  9. Process appropriate input data formats including, but not limited to, strategic imagery, elevation-data, vector-data, passive/active point cloud, triangular/polygonal mesh, etc. (T/O).

PHASE I:

The objective of this SOCOM Phase I SBIR effort is to conduct and document the results of a thorough feasibility study to investigate what is in the art of the possible within the determined trade space that will satisfy the requirements specified by this topic. As a part of this feasibility study, respondents shall investigate all viable system design options and meet or exceed the performance parameter specifications provided herein. It shall also consider programmatic, schedule, and technical risks and potential payoffs of the innovative technology options that are investigated culminating in a recommended development strategy that best achieves the objectives of this technology pursuit.


Government funds obligated on Phase I SBIR contracts are to be used for the sole purpose of conducting a thorough feasibility study using scientific experiments and laboratory studies as necessary. Operational prototypes shall not be developed with SOCOM funds during Phase I feasibility studies. If an operational prototype is developed during Phase I with funding from sources other than the SBIR award, that prototype will influence the Government's whether and with whom to pursue a Phase II effort.

PHASE II:

Develop, install, and demonstrate a prototype system determined, during the Phase I feasibility study, to be the most feasible and efficacious solution to this technology pursuit. Phase II will likely include additional performance and technical requirements developed during, or revealed by, Phase I investigations. In addition, as a system intended for operational evaluation, the Phase II prototype may be required to satisfy security requirements that will allow its implementation and use on the SOF information enterprise.

PHASE III:

Once adequately matured, this system would be used in a broad range of military, Government, and commercial applications where it is desirable to construct detailed, OGC CDB compliant databases for use in terrestrial modeling, visualization, and simulation. This capability addresses the intersection of simulation and gaming and has the potential to rapidly move the commercial gaming industry out of artistically rendered fantasy and into the real world.

KEYWORDS: Open Geospatial Consortium, OGC, Common Data Base, CDB, Imagery Analysis, Imagery, Geospatial Intelligence, GEOINT, point cloud, mesh, terrain, decimation

References:

[9] How Mobility Solutions are Transforming Military Tactical Operations and Driving Better Mission Outcomes, https://insights.samsung.com/2018/12/13/how-mobility-solutions-are-transforming-military-tactical-operations-driving-better-mission-outcomes/, accessed 30 May 2019

[8] Mobile Awareness GEOINT Environment, http://ngageoint.github.io/MAGE/, accessed 30 May 2019

[7] Why is the OGC Involved in Sensor Webs?, http://www.opengeospatial.org/domain/swe, accessed 30 May 2019

[6] Integrated Sensor Architecture, https://www.cerdec.army.mil/news_and_media/Integrate_Sensor_Architecture/, accessed 30 May 2019

[5] NoCloud: Exploring Network Disconnection through On-Device Data Analysis, https://www.cs.dartmouth.edu/~dfk/papers/reza-nocloud.pdf, accessed 30 May 2019

[4] Open Sensor Hub, Fun Times, and the Future of the Internet of Things, https://opensensorhub.org/2016/02/05/opensensorhub-funtimes-and-the-future-of-the-internet-of-things/, accessed 30 May 2019

[3] The Hyper Enabled Operator, Small Wars Journal, https://smallwarsjournal.com/jrnl/art/hyper-enabled-operator#_edn2, accessed 30 May 2019

[2] Overview of the OGC CDB Standard for 3D Synthetic Environment Modeling and Simulation, Saeedi, S., Liang, S., Graham, D., Lokuta, M.F., Mostafavi, M.A. International Society for Photogrammetry and Remote Sensing, International Journal of Geo-Information. 2017, 6, 306. https://www.mdpi.com/2220-9964/6/10/306

[1] Open Geospatial Consortium, CDB Standard, http://www.opengeospatial.org/standards/cdb

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