Encoding and analyzing aerial imagery using geospatial semantic graphs

Watson, Jean-Paul; Strip, David R.; McLendon, William C.; Parekh, Ojas D.; Diegert, Carl F.; Martin, Shawn Bryan; Rintoul, Mark Daniel

doi:10.2172/1204099

Title: Encoding and analyzing aerial imagery using geospatial semantic graphs

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Abstract

While collection capabilities have yielded an ever-increasing volume of aerial imagery, analytic techniques for identifying patterns in and extracting relevant information from this data have seriously lagged. The vast majority of imagery is never examined, due to a combination of the limited bandwidth of human analysts and limitations of existing analysis tools. In this report, we describe an alternative, novel approach to both encoding and analyzing aerial imagery, using the concept of a geospatial semantic graph. The advantages of our approach are twofold. First, intuitive templates can be easily specified in terms of the domain language in which an analyst converses. These templates can be used to automatically and efficiently search large graph databases, for specific patterns of interest. Second, unsupervised machine learning techniques can be applied to automatically identify patterns in the graph databases, exposing recurring motifs in imagery. We illustrate our approach using real-world data for Anne Arundel County, Maryland, and compare the performance of our approach to that of an expert human analyst.

Authors:

Watson, Jean-Paul ^[1]; Strip, David R. ^[1]; McLendon, William C. ^[1]; Parekh, Ojas D. ^[1]; Diegert, Carl F. ^[1]; Martin, Shawn Bryan ^[1]; Rintoul, Mark Daniel ^[1]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Publication Date:: Sat Feb 01 00:00:00 EST 2014

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1204099

Report Number(s):: SAND2014-1405
504876

DOE Contract Number:: AC04-94AL85000

Resource Type:: Technical Report

Country of Publication:: United States

Language:: English

Citation Formats


                    Watson, Jean-Paul, Strip, David R., McLendon, William C., Parekh, Ojas D., Diegert, Carl F., Martin, Shawn Bryan, and Rintoul, Mark Daniel. Encoding and analyzing aerial imagery using geospatial semantic graphs.  United States: N. p., 2014. 
        Web.  doi:10.2172/1204099.

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                    Watson, Jean-Paul, Strip, David R., McLendon, William C., Parekh, Ojas D., Diegert, Carl F., Martin, Shawn Bryan, & Rintoul, Mark Daniel. Encoding and analyzing aerial imagery using geospatial semantic graphs.  United States.  https://doi.org/10.2172/1204099

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                    Watson, Jean-Paul, Strip, David R., McLendon, William C., Parekh, Ojas D., Diegert, Carl F., Martin, Shawn Bryan, and Rintoul, Mark Daniel. 2014.  
        "Encoding and analyzing aerial imagery using geospatial semantic graphs".  United States.  https://doi.org/10.2172/1204099.  https://www.osti.gov/servlets/purl/1204099.

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@article{osti_1204099,

  title        = {Encoding and analyzing aerial imagery using geospatial semantic graphs},

  author       = {Watson, Jean-Paul and Strip, David R. and McLendon, William C. and Parekh, Ojas D. and Diegert, Carl F. and Martin, Shawn Bryan and Rintoul, Mark Daniel},

  abstractNote = {While collection capabilities have yielded an ever-increasing volume of aerial imagery, analytic techniques for identifying patterns in and extracting relevant information from this data have seriously lagged. The vast majority of imagery is never examined, due to a combination of the limited bandwidth of human analysts and limitations of existing analysis tools. In this report, we describe an alternative, novel approach to both encoding and analyzing aerial imagery, using the concept of a geospatial semantic graph. The advantages of our approach are twofold. First, intuitive templates can be easily specified in terms of the domain language in which an analyst converses. These templates can be used to automatically and efficiently search large graph databases, for specific patterns of interest. Second, unsupervised machine learning techniques can be applied to automatically identify patterns in the graph databases, exposing recurring motifs in imagery. We illustrate our approach using real-world data for Anne Arundel County, Maryland, and compare the performance of our approach to that of an expert human analyst.},

  doi          = {10.2172/1204099},

  url          = {https://www.osti.gov/biblio/1204099},
  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {Sat Feb 01 00:00:00 EST 2014},

  month        = {Sat Feb 01 00:00:00 EST 2014}

}

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