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Bayesian Urban Degradation Assessment

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047618C0043
Agency Tracking Number: NGA-P1-18-09
Amount: $99,997.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA181-004
Solicitation Number: 2018.1
Solicitation Year: 2018
Award Year: 2018
Award Start Date (Proposal Award Date): 2018-09-06
Award End Date (Contract End Date): 2019-06-15
Small Business Information
1845 West 205th Street
Torrance, CA 90501
United States
DUNS: 080921977
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Oleg Galkin
 (310) 320-1827
Business Contact
 Laura Miller
Phone: (310) 320-1827
Research Institution

To address the NGA need for algorithms that fuse observables from over-flight operations and from ground sources to automatically estimatethe degradation of urban environments due to battle damage or natural disasters, Intellisense Systems, Inc. (ISS) proposes to develop a newBayesian Urban Degradation Assessment (BUDA) software system. It is based on the integration of multiple damage assessment algorithmsutilizing different data sources and implementation of a Bayesian inference engine for optimal data fusion of heterogeneous and multiscaledata for damage estimation of urban environments. Specifically, the novel system architecture integrating multiple concurrent algorithmsallows optimal 3D assessment of real-world observables from a wide variety of input data sources, and a new implementation of a Bayesianinference engine implements optimal data fusion, time-critical knowledge management/updates, and confidence estimation. In Phase I, ISSwill demonstrate the feasibility of BUDA by developing a proof-of-concept BUDA system and showing its capability to achieve optimal datafusion from different sources of overhead and ground imagery. In Phase II, ISS will develop a fully functional BUDA system capable ofautomatic assessment of urban environment degradation. We will demonstrate the increased accuracy of the fused data estimates ascompared to those derived from a single source.

* Information listed above is at the time of submission. *

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