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

doi:10.2172/1254282

GSTDTAP > 地球科学

DOI	10.2172/1254282
报告编号	SAND2016-4557
来源ID	OSTI ID: 1254282
	Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs
	William C. McLendon III; Brost, Randy C.
	2016-05-01
出版年	2016
页数	58
语种	英语
国家	美国
领域	地球科学
英文摘要	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.
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来源平台	US Department of Energy (DOE)
引用统计
文献类型	科技报告
条目标识符	http://119.78.100.173/C666/handle/2XK7JSWQ/7334
专题	地球科学
推荐引用方式 GB/T 7714	William C. McLendon III,Brost, Randy C.. Path Network Recovery Using Remote Sensing Data and Geospatial-Temporal Semantic Graphs,2016.