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Title: Geospatialtemporal remote sensing analysis using semantic graphs.

Abstract

Abstract not provided.

Authors:
;
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1294123
Report Number(s):
SAND2012-10504C
493277
DOE Contract Number:
AC04-94AL85000
Resource Type:
Conference
Resource Relation:
Conference: Proposed for presentation at the Nuclear Weaponization and Material Production Detection (MPD) Team Review held January 15-17, 2013 in Livermore, CA.
Country of Publication:
United States
Language:
English

Citation Formats

Brost, Randolph C., and Watson, Jean-Paul. Geospatialtemporal remote sensing analysis using semantic graphs.. United States: N. p., 2012. Web.
Brost, Randolph C., & Watson, Jean-Paul. Geospatialtemporal remote sensing analysis using semantic graphs.. United States.
Brost, Randolph C., and Watson, Jean-Paul. Sat . "Geospatialtemporal remote sensing analysis using semantic graphs.". United States. doi:. https://www.osti.gov/servlets/purl/1294123.
@article{osti_1294123,
title = {Geospatialtemporal remote sensing analysis using semantic graphs.},
author = {Brost, Randolph C. and Watson, Jean-Paul},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sat Dec 01 00:00:00 EST 2012},
month = {Sat Dec 01 00:00:00 EST 2012}
}

Conference:
Other availability
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  • Remote sensing systems produce large volumes of high-resolution images that are difficult to search. The GeoGraphy (pronounced Geo-Graph-y) framework [2, 20] encodes remote sensing imagery into a geospatial-temporal semantic graph representation to enable high level semantic searches to be performed. Typically scene objects such as buildings and trees tend to be shaped like blocks with few holes, but other shapes generated from path networks tend to have a large number of holes and can span a large geographic region due to their connectedness. For example, we have a dataset covering the city of Philadelphia in which there is a singlemore » road network node spanning a 6 mile x 8 mile region. Even a simple question such as "find two houses near the same street" might give unexpected results. More generally, nodes arising from networks of paths (roads, sidewalks, trails, etc.) require additional processing to make them useful for searches in GeoGraphy. We have assigned the term Path Network Recovery to this process. Path Network Recovery is a three-step process involving (1) partitioning the network node into segments, (2) repairing broken path segments interrupted by occlusions or sensor noise, and (3) adding path-aware search semantics into GeoQuestions. This report covers the path network recovery process, how it is used, and some example use cases of the current capabilities.« less
  • Abstract not provided.
  • Abstract not provided.
  • Various technologies for facilitating analysis of large remote sensing and geolocation datasets to identify features of interest are described herein. A search query can be submitted to a computing system that executes searches over a geospatial temporal semantic (GTS) graph to identify features of interest. The GTS graph comprises nodes corresponding to objects described in the remote sensing and geolocation datasets, and edges that indicate geospatial or temporal relationships between pairs of nodes in the nodes. Trajectory information is encoded in the GTS graph by the inclusion of movable nodes to facilitate searches for features of interest in the datasetsmore » relative to moving objects such as vehicles.« less