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GeoVisipedia is a new and novel approach to annotating satellite imagery. It uses wiki pages to annotate objects rather than simple labels. The use of wiki pages to contain annotations is particularly useful for annotating objects in imagery of complex geospatial configurations such as industrial facilities. GeoVisipedia uses the PRISM algorithm to project annotations applied to one image to other imagery, hence enabling ubiquitous annotation. This paper derives the PRISM algorithm, which uses image metadata and a 3D facility model to create a view matrix unique to each image. The view matrix is used to project model components onto a mask which aligns the components with the objects in the scene that they represent. Wiki pages are linked to model components, which are in turn linked to the image via the component mask. An illustration of the efficacy of the PRISM algorithm is provided, demonstrating the projection of model components onto an effluent stack. We conclude with a discussion of the efficiencies of GeoVisipedia over manual annotation, and the use of PRISM for creating training sets for machine learning algorithms.

GeoVisipedia (Geospatial Visual Wikipedia) is a new and novel approach to sharing knowledge about complex geospatial entities such as facilities. Facilities are composed of interconnected objects such as buildings, chemical processing units, electrical generation equipment and similar structures. Satellite imagery of a facility reveals a great deal about the organization and visual appearance of objects in a facility, but very little about the identity or function of the object. For example, given a satellite imagery of an oil refinery, an expert in refining readily identifies distillation units and can explain how they work. A non-expert would have a very difficult time identifying these objects let alone explaining how they function. To make highly complex information accessible to non-experts, GeoVisipedia associates a wiki page with objects in satellite imagery. A user selects an object in the image and a wiki page appears that provides the user with detailed information about the object. Experts can author information into the wiki and this information is shared with other users. Additionally, GeoVisipedia automatically transfers all wiki pages from one image of a facility to other imagery of the facility. Consequentially, knowledge about objects in the facility integrates over time as new imagery becomes available and as new wiki pages are created and additional information is added to existing wiki pages. In this respect, satellite imagery becomes a portal to expert knowledge and insight about objects in a facility.

Protecting groundwater is of growing interest as pressure on these resources grows. Recharge of groundwater takes place through the vadose zone, where complex interactions between thermal-hydrological-geochemical processes affect water quality. Monitoring processes in the vadose zone is an important means of evaluating the long-term health of aquifer systems, and has become an integral part of many subsurface engineering efforts. Monitoring such systems, however, may be affected by changes in climate that slowly propagate through vadose zone systems. We describe in this paper the use of NUFT-C, a reactive transport simulator designed to run on a high performance, massively parallel computer, to compare quantitatively the evolution of a deep vadose zone with changes expected from an engineered high-level nuclear waste repository. The results suggest that the impacts from waste emplacement are, in some instances, similar to those that would be observed as a result of climate change, whereas others are distinguishable from evolution of the natural system. Such simulations facilitate design of long-term monitoring programs that take account of these complex effects. The results emphasize the importance of developing long-term baseline measurements and control sites, in order to enhance confidence in interpretations of complexly evolving data sets that will be obtained from multidecade monitoring efforts.

Chloride mass balance, and stable (deuterium and 18 O) and radiogenic ( 3 H, 36 Cl) isotope studies of deep vadose zone pore waters have generally concluded that variations in moisture flux can account for the observed variations in abundance of these approximately conservative tracers. It can be inferred, on the basis of these observations and interpretations, that a climate change record is preserved in these vadose zone waters. In arid regions where thick (>100 m) vadose zones persist, it has been concluded that this record may extend back more than 100 000 yr. Consideration of the mechanisms that control reactive transport led to the conclusion that such climate-driven effects will also be evident as chemical reactions involving dissolution and/or precipitation of mineral phases along the flow pathway. As a result, there should also be variations in the concentrations of nonconservative chemical species that correspond to changes in the concentrations of the conservative tracers. Simulations of this reactive transport, in a regime typical of the arid U.S. Southwest, demonstrate that these changes can modify pore water chemistry by factors of up to 200%, but the changes take place slowly, requiring thousands of years to achieve steady-state conditions. This suggests that a very rich archive of climate change history is preserved in this type of setting. However, extracting that history is currently hampered by limitations in data and models (e.g., effective mineral reactive surface areas, fluid flow pathways, and quantified models of wetted fracture surface in unsaturated, fractured systems). This challenge may be overcome if coordinated efforts are undertaken that exploit the power of detailed studies of isotope systematics, microscale rock characterization, and high performance computing.

Gradient direction matching (GDM) is the main target identification algorithm used in the Image Content Engine project at Lawrence Livermore National Laboratory. GDM is a 3D solid model-based edge-matching algorithm which does not require explicit edge extraction from the source image. The GDM algorithm is presented, identifying areas where performance enhancement seems possible. Improving the process of producing model gradient directions from the solid model by assigning different weights to different parts of the model is an extension tested in the current study. Given a simple geometric model, we attempt to determine, without obvious semantic clues, if different weight values produce significantly better matching accuracy, and how those weights should be assigned to produce the best matching accuracy. Two simple candidate strategies for assigning weights are proposed--pixel-weighted and edge-weighted. We adjust the weights of the components in a simple model of a tractor/semi-trailer using relevance feedback to produce an optimal set of weights for this model and a particular test image. The optimal weights are then compared with pixel and edge-weighting strategies to determine which is most suitable and under what circumstances.

The Image Content Engine (ICE) is being developed to provide cueing assistance to human image analysts faced with increasingly large and intractable amounts of image data. The ICE architecture includes user configurable feature extraction pipelines which produce intermediate feature vector and match surface files which can then be accessed by interactive relational queries. Application of the feature extraction algorithms to large collections of images may be extremely time consuming and is launched as a batch job on a Linux cluster. The query interface accesses only the intermediate files and returns candidate hits nearly instantaneously. Queries may be posed for individual objects or collections. The query interface prompts the user for feedback, and applies relevance feedback algorithms to revise the feature vector weighting and focus on relevant search results. Examples of feature extraction and both model-based and search-by-example queries are presented.

Regulatory requirements and scientific needs require that the response of the geological system to emplacement of high level radioactive waste be monitored for long time periods. This monitoring activity is intended to establish the extent to which predicted behavior matches the actual response of the geological system to waste emplacement. To accomplish this goal, field measurements must be made at a spatial resolution that will determine whether the changes in parameters that are monitored conform to predicted evolutionary patterns. From the perspective of thermohydrological and geochemical parameters, key measurements will consider pore water compositional evolution and changes in matrix and fracture saturation in the near vicinity of waste emplacement tunnels. A massively parallel high performance computational platform (a 1200 processor IBM SP-2) was used to conduct three-dimensional, high resolution simulations to ascertain the spatial variability to be expected during a monitoring period. The results show that spatial variability in certain chemical parameters below waste emplacement tunnels provides robust targets for monitoring, but will require sampling on the scale of 10 s of centimeters in some locations, in order to rigorously test models. Chemical variability induced by relatively small changes in waste package heat output suggests that designing a monitoring program that will rigorously test model predictions will likely require high resolution, three-dimensional simulations of the “as-built” monitoring tunnels.