67 research outputs found

    Visual Techniques for Geological Fieldwork Using Mobile Devices

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    Visual techniques in general and 3D visualisation in particular have seen considerable adoption within the last 30 years in the geosciences and geology. Techniques such as volume visualisation, for analysing subsurface processes, and photo-coloured LiDAR point-based rendering, to digitally explore rock exposures at the earth’s surface, were applied within geology as one of the first adopting branches of science. A large amount of digital, geological surface- and volume data is nowadays available to desktop-based workflows for geological applications such as hydrocarbon reservoir exploration, groundwater modelling, CO2 sequestration and, in the future, geothermal energy planning. On the other hand, the analysis and data collection during fieldwork has yet to embrace this ”digital revolution”: sedimentary logs, geological maps and stratigraphic sketches are still captured in each geologist’s individual fieldbook, and physical rocks samples are still transported to the lab for subsequent analysis. Is this still necessary, or are there extended digital means of data collection and exploration in the field ? Are modern digital interpretation techniques accurate and intuitive enough to relevantly support fieldwork in geology and other geoscience disciplines ? This dissertation aims to address these questions and, by doing so, close the technological gap between geological fieldwork and office workflows in geology. The emergence of mobile devices and their vast array of physical sensors, combined with touch-based user interfaces, high-resolution screens and digital cameras provide a possible digital platform that can be used by field geologists. Their ubiquitous availability increases the chances to adopt digital workflows in the field without additional, expensive equipment. The use of 3D data on mobile devices in the field is furthered by the availability of 3D digital outcrop models and the increasing ease of their acquisition. This dissertation assesses the prospects of adopting 3D visual techniques and mobile devices within field geology. The research of this dissertation uses previously acquired and processed digital outcrop models in the form of textured surfaces from optical remote sensing and photogrammetry. The scientific papers in this thesis present visual techniques and algorithms to map outcrop photographs in the field directly onto the surface models. Automatic mapping allows the projection of photo interpretations of stratigraphy and sedimentary facies on the 3D textured surface while providing the domain expert with simple-touse, intuitive tools for the photo interpretation itself. The developed visual approach, combining insight from all across the computer sciences dealing with visual information, merits into the mobile device Geological Registration and Interpretation Toolset (GRIT) app, which is assessed on an outcrop analogue study of the Saltwick Formation exposed at Whitby, North Yorkshire, UK. Although being applicable to a diversity of study scenarios within petroleum geology and the geosciences, the particular target application of the visual techniques is to easily provide field-based outcrop interpretations for subsequent construction of training images for multiple point statistics reservoir modelling, as envisaged within the VOM2MPS project. Despite the success and applicability of the visual approach, numerous drawbacks and probable future extensions are discussed in the thesis based on the conducted studies. Apart from elaborating on more obvious limitations originating from the use of mobile devices and their limited computing capabilities and sensor accuracies, a major contribution of this thesis is the careful analysis of conceptual drawbacks of established procedures in modelling, representing, constructing and disseminating the available surface geometry. A more mathematically-accurate geometric description of the underlying algebraic surfaces yields improvements and future applications unaddressed within the literature of geology and the computational geosciences to this date. Also, future extensions to the visual techniques proposed in this thesis allow for expanded analysis, 3D exploration and improved geological subsurface modelling in general.publishedVersio

    Usability-enhanced coordination design of geovisualisations to communicate coastal flood risk information

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    For at least two millennia and probably much longer, the traditional vehicle for communicating geographical information to end-users has been the map. With the advent of computers, the means of both producing and consuming maps have radically been transformed, while the inherent nature of the information product has also expanded and diversified rapidly. This has given rise in recent years to the new concept of geovisualisation (GVIS), which draws on the skills of the traditional cartographer, but extends them into three spatial dimensions and may also add temporality, photorealistic representations and/or interactivity. Demand for GVIS technologies and their applications has increased significantly in recent years, driven by the need to study complex geographical events and in particular their associated consequences and to communicate the results of these studies to a diversity of audiences and stakeholder groups. GVIS has data integration, multi-dimensional spatial display advanced modelling techniques, dynamic design and development environments and field-specific application needs. To meet with these needs, GVIS tools should be both powerful and inherently usable, in order to facilitate their role in helping interpret and communicate geographic problems. However no framework currently exists for ensuring this usability. The research presented here seeks to fill this gap, by addressing the challenges of incorporating user requirements in GVIS tool design. It starts from the premise that usability in GVIS should be incorporated and implemented throughout the whole design and development process. To facilitate this, Subject Technology Matching (STM) is proposed as a new approach to assessing and interpreting user requirements. Based on STM, a new design framework called Usability Enhanced Coordination Design (UECD) is ten presented with the purpose of leveraging overall usability of the design outputs. UECD places GVIS experts in a new key role in the design process, to form a more coordinated and integrated workflow and a more focused and interactive usability testing. To prove the concept, these theoretical elements of the framework have been implemented in two test projects: one is the creation of a coastal inundation simulation for Whitegate, Cork, Ireland; the other is a flooding mapping tool for Zhushan Town, Jiangsu, China. The two case studies successfully demonstrated the potential merits of the UECD approach when GVIS techniques are applied to geographic problem solving and decision making. The thesis delivers a comprehensive understanding of the development and challenges of GVIS technology, its usability concerns, usability and associated UCD; it explores the possibility of putting UCD framework in GVIS design; it constructs a new theoretical design framework called UECD which aims to make the whole design process usability driven; it develops the key concept of STM into a template set to improve the performance of a GVIS design. These key conceptual and procedural foundations can be built on future research, aimed at further refining and developing UECD as a useful design methodology for GVIS scholars and practitioners

    Remote Sensing for Land Administration 2.0

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    The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

    Extending DoD modeling and simulation with Web 2.0, Ajax and X3D

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    DoD has much to gain from Web 2.0 and the Ajax paradigm in open source. The Java language has come a long way in providing real world case studies and scalable solutions for the enterprise that are currently in production on sites such as eBay.com (http://www.ebay.com) and MLB.com (http://www.mlb.com). The most popular Ajax application in production is Google Maps (http://maps.google.com), which serves as a good example of the power of the technology. Open Source technology has matured greatly in the past three years and is now mature enough for deployment within DoD systems. In the past, management within the DoD has been reluctant to consider Enterprise Level Open Source Technologies as a solution, fearing that they might receive little to no support. In fact, the Open Source Business Model is entirely based on first developing a broad user base then providing support as a service for their clients. DoD Modeling and Simulation can create dynamic and compelling content that is ready for the challenges of the 21st century and completely integrated with the Global Information Grid (GIG) concept. This paper presents a short history of Model View Controller (MVC) architectures and goes over various pros and cons of each framework (Struts, Spring, Java Server Faces), which is critical for the deployment of a modern Java web application. Ajax and various frameworks are then discussed (Dojo, Google Web Toolkit, ZK, and Echo2). The paper then touches on Ajax3D technologies and the use of Rez to generate 3D models of entire cities and goes on to discuss possible extended functionality of the Rez concept to create a terrain system like Google Earth in X3D-Earth.http://archive.org/details/extendingdodmode109453282US Navy (USN) author.Approved for public release; distribution is unlimited

    Labeling, discovering, and detecting objects in images

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    Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (p. 131-138).Recognizing the many objects that comprise our visual world is a difficult task. Confounding factors, such as intra-class object variation, clutter, pose, lighting, dealing with never-before seen objects, scale, and lack of visual experience often fool existing recognition systems. In this thesis, we explore three issues that address a few of these factors: the importance of labeled image databases for recognition, the ability to discover object categories from simply looking at many images, and the use of large labeled image databases to efficiently detect objects embedded in scenes. For each of the issues above, we will need to cope with large collections of images. We begin by introducing LabelMe, a large labeled image database collected from users via a web annotation tool. The users of the annotation tool provided information about the identity, location, and extent of objects in images. Through this effort, we have collected about 160,000 images and 200,000 object labels to date. We show that the database spans more object categories and scenes and offers a wider range of appearance variation than most other labeled databases for object recognition. We also provide four useful extensions of the database: (i) resolving synonym ambiguities that arise in the object labels, (ii) recovering object-part relationships, (iii) extracting a depth ordering of the labeled objects in an image, and (iv) providing a semi-automatic process for the fast labeling of images. We then seek to learn models of objects in the extreme case when no supervision is provided. We draw inspiration from the success of unsupervised topic discovery in text. We apply the Latent Dirichlet Allocation model of Blei et al. to unlabeled images to automatically discover object categories. To achieve this, we employ the visual words representation of images, which is analogous to the words in text.(cont) We show that our unsupervised model achieves comparable classification performance to a model trained with supervision on an unseen image set depicting several object classes. We also successfully localize the discovered object classes in images. While the image representation used for the object discovery process is simple to compute and can distinguish between different object categories, it does not capture explicit spatial information about regions in different parts of the image. We describe a procedure for combining image segmentation with the object discovery process toby Bryan Christopher Russell.Ph.D

    Augmented Reality

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    Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

    Application of GIS and Spatial Data Modeling to Archaeology: A Case Study in the American Southwest

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    One of the most important methodological advances in the archaeology of the past quarter century is the use of Geographic Information System (GIS) in archaeological research. Within this time frame, GIS has evolved from an emergent geospatial technology with limited mapmaking capabilities to a technology of choice for cultural resource managers, planners, and academic archaeologists alike. This dissertation examines the evolutionary trajectory and impact of GIS in the discipline since its introduction, and its potential to support new applications of GIS-driven innovation in archaeological research. As part of this project, two separate studies were conducted. The first study assessed adoption and diffusion trends for the technology based on the published literature from 1987-2010 using bibliometric and content analysis. These results suggest that despite adoption reaching a critical mass point in 2003-2006, GIS use is still maturing, and emphasis continues to be on methodological refinements rather than theoretical advances. Many of the technical developments coincide with larger changes within computing and in the convergence of technologies and platforms within the GIS industry. Recent publications, however, indicate the emergence of a possibly new direction for archaeological research which relies more on computationally intensive rather than empirical methods of investigation, in effect blurring traditional distinctions between method and theory. The second study conducted as part of this project explores the implications of this phenomenon by developing and implementing an application within a GIS environment for knowledge discovery in databases. The objective was to explore the feasibility and efficacy of geographic data mining using current technologies and archaeological data standards, identify barriers to its implementation, and demonstrate a new course for GIS-driven innovation in the field. Various archaeological and environmental datasets from the Fort Wingate Depot Activity in western New Mexico, USA were selected, compiled, prepared, and analyzed as part of the case study. Logistic regression was combined with Weights-of-Evidence modeling to discover previously unknown but statistically significant relationships and patterns within the prehistoric and historic data. This study offers suggestions on both how to adapt old data to new technologies and how to adapt new technologies to new ways of thinking

    An appearance-driven method for converting polygon soup building models for 3D geospatial applications

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    Polygon soup building models are fine for visualization purposes such as in games and movies. They, however, are not suitable for 3D geospatial applications which require geometrical analysis, since they lack connectivity information and may contain intersections internally between their parts. In this paper, we propose an appearance-driven method to interactively convert an input polygon soup building model to a two-manifold mesh, which is more suitable for 3D geospatial applications. Since a polygon soup model is not suitable for geometrical analysis, our key idea is to extract and utilize the visual appearance of the input building model for the conversion. We extract the silhouettes and use them to identify the features of the building. We then generate horizontal cross sections based on the locations of the features and then reconstruct the building by connecting two neighbouring cross sections. We propose to integrate various rasterization techniques to facilitate the conversion. Experimental results show the effectiveness of the proposed method.NRF (Natl Research Foundation, S’pore)Accepted versio

    Bioinspired metaheuristic algorithms for global optimization

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    This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

    Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

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    In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF
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