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Pope, Paul A.

Publications and source records attributed to Pope, Paul A..

7 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

A morphology independent methodology for quantifying planview river change and characteristics from remotely sensed imagery

Remotely sensed imagery of rivers has long served as a means for characterizing channel properties and detection of planview change. In the last decade the dramatic increase in the availability of satellite imagery and processing tools has created the potential to greatly expand the spatial and temporal scale of our understanding of river morphology and dynamics. To date, the majority of GIS and automated analyses of planview changes in rivers from remotely sensed data has been developed for single-threaded meandering river systems. These methods have limited applicability to many of the earth's rivers with complex multi-channel planforms. Here we present the methodologies of a set of analysis algorithms collectively called Spatially Continuous Riverbank Erosion and Accretion Measurements (SCREAM). SCREAM analyzes planview river metrics regardless of river morphology. These algorithms quantify both the erosion and accretion rates of riverbanks from binary masks of channels generated from imagery acquired at two time periods. Additionally, the program quantifies the area of change between river channels and the surrounding floodplain and area of islands lost or formed between these two time periods. To examine variations in erosion rates in relation to local channel attributes and make rate comparisons between river systems of varying sizes, the program determines channel widths and bank curvature at every bank pixel. SCREAM was developed and tested on rivers with diverse and complex planform morphologies in imagery acquired from a range of observational platforms with varying spatial resolutions. Here, validation and verification of SCREAM-generated metrics against manual measurements show no significant measurement errors in determination of channel width, erosion, and bank aspects. SCREAM has the potential to provide data for both the quantitative examination of the controls on erosion rates and for the comparison of these rates across river systems ranging broadly in size and planform morphology.

47 OTHER INSTRUMENTATION↗
DOE OSTI2013

Benchmark Imagery for Assessing Geospatial Semantic Content Extraction Algorithms (Final Report)

Benchmark Imagery for Assessing Geospatial Semantic Content Extraction Algorithms (the “Benchmark Imagery Project”) was a three-year endeavor funded by the Enabling Capabilities (EC) Team of DOE/NNSA’s Office of Proliferation Detection R&D, NA-22, and carried out through a collaborative effort between Los Alamos National Laboratory (LANL) and the Lawrence Livermore National Laboratory (LLNL). The primary objective was to create a set of benchmark imagery for validating algorithms designed to extract semantic content from geospatial images of industrial facilities. The goal was not to develop algorithms; rather, it was to provide information that could be used to do so. The secondary objective was to demonstrate how this information could be used for algorithm validation. This report summarizes the results of the Benchmark Imagery Project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2013

Benchmark Imagery Project: Selection of Real World Images for Creating Composite Images of Facilities

The "Benchmark Imagery" project is a DOE NA-22-funded effort whose three-year goal is to create a suite of imagery (both real and composite) that can be used to test geospatial algorithms used for extracting semantic content from overhead images of industrial facilities. A composite image is an image that contains both real elements such as photographic imagery and synthetic elements such as 3D models. Composite images have the advantage of being customizable and distributable, and they can contain complete ground truth. It is our goal to generate composite images which appear realistic to both human and algorithm. When generating composite images of facilities we use real overhead imagery to increase realism and decrease effort. To construct all elements with purely synthetic textures would require too much effort and still may not look as realistic as a real image. We use real time 3D rendering software "SceneWorks" for rendering composite images. In SceneWorks, a scene is constructed by placing 3D models of facility components on top of a base terrain. A base terrain is a 3D model constructed from digital elevation mapping (DEM) data and then textured with overhead orthorectified imagery. The base terrain is constructed to be geospatially accurate using a real location’s source data (DEM and Imagery.) This document outlines the selection criteria in selecting appropriate location and source data for constructing the base terrain. Imagery should have resolution equal or better than desired composite image resolution. The imagery should have no seams. Seams can occur when two or more different imagery data sets exist in a single area with neither set covering the entire area. Elevation data (DEM) resolution generally should be 1/9 arc second or better depending on the variability of elevation. The imagery must contain a flat clearing of land large enough for multiple facility types. There should be no high-rise buildings anywhere in the base terrain.

42 ENGINEERING↗
DOE OSTI2012

Benchmark Imagery for Assessing Geospatial Semantic Content Extraction Algorithms -- Annual Report for FY2012

The primary objective of this project is to create a set of benchmark imagery for validating algorithms designed to extract semantic content from geospatial images of industrial facilities. The goal is not to develop algorithms but to provide images that can be used to do so. The secondary objective is to demonstrate how this imagery could be used for algorithm validation. The project is a collaborative effort between Los Alamos National Laboratory (LANL) and the Lawrence Livermore National Laboratory (LLNL).

42 ENGINEERING↗
DOE OSTI2012

Benchmark Imagery Project: Report on Generation of Synthetic Images

The “Benchmark Imagery” project is a DOE NA-22-funded effort whose three-year goal is to create a suite of imagery that can be used to test geospatial algorithms used for extracting semantic content from overhead images of industrial facilities (e.g., auto-annotation). The project is a collaborative effort between Los Alamos National Laboratory (LANL) and the Lawrence Livermore National Laboratory (LLNL). Although a variety of image modalities are used to capture overhead photographs of terrestrial facilities, the current focus of the effort of this project is on collecting and annotating conventional visual-band (RGB or RGB/NIR) images. Presently, the set of images consists of 114 airborne images of various US industrial facilities. We have annotated all of the images in the imagery set, using a constrained vocabulary of about a dozen general terms such as “building,” ”tower,” or “electrical substation.” The project plans to include synthetic and composite images in addition to actual photographs. In preparation for using synthetic images, we have built a proof-of-principle synthetic image of a fictitious nuclear power plant. This report describes the key aspects of creating that image.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

GSV Annotated Bibliography

The following annotated bibliography was developed as part of the Geospatial Algorithm Veri cation and Validation (GSV) project for the Simulation, Algorithms and Modeling program of NA-22. Veri cation and Validation of geospatial image analysis algorithms covers a wide range of technologies. Papers in the bibliography are thus organized into the following ve topic areas: Image processing and analysis, usability and validation of geospatial image analysis algorithms, image distance measures, scene modeling and image rendering, and transportation simulation models.

99 GENERAL AND MISCELLANEOUS↗