skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs

Abstract

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 single 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 themore » path network recovery process, how it is used, and some example use cases of the current capabilities.« less

Authors:
 [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1254282
Report Number(s):
SAND2016-4557
640196
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; 58 GEOSCIENCES

Citation Formats

William C. McLendon III, and Brost, Randy C. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs. United States: N. p., 2016. Web. doi:10.2172/1254282.
William C. McLendon III, & Brost, Randy C. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs. United States. https://doi.org/10.2172/1254282
William C. McLendon III, and Brost, Randy C. 2016. "Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs". United States. https://doi.org/10.2172/1254282. https://www.osti.gov/servlets/purl/1254282.
@article{osti_1254282,
title = {Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs},
author = {William C. McLendon III and Brost, Randy C.},
abstractNote = {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 single 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.},
doi = {10.2172/1254282},
url = {https://www.osti.gov/biblio/1254282}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2016},
month = {5}
}