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Title: LDRD final report :

Abstract

Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report a preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1088081
Report Number(s):
SAND2013-0724
452486
DOE Contract Number:
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English

Citation Formats

Brost, Randolph C., and McLendon, William Clarence,. LDRD final report :. United States: N. p., 2013. Web. doi:10.2172/1088081.
Brost, Randolph C., & McLendon, William Clarence,. LDRD final report :. United States. doi:10.2172/1088081.
Brost, Randolph C., and McLendon, William Clarence,. Tue . "LDRD final report :". United States. doi:10.2172/1088081. https://www.osti.gov/servlets/purl/1088081.
@article{osti_1088081,
title = {LDRD final report :},
author = {Brost, Randolph C. and McLendon, William Clarence,},
abstractNote = {Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report a preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.},
doi = {10.2172/1088081},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Tue Jan 01 00:00:00 EST 2013},
month = {Tue Jan 01 00:00:00 EST 2013}
}

Technical Report:

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