3D Camera Tracking for Mixed Reality using Multi-Sensors Technology

The concept of Mixed Reality (MR) aims at completing our perception of the real world, by adding fictitious elements that are not perceptible naturally such as: computer generated images, virtual objects, texts, symbols, graphics, sounds, smells, et cetera. One of the major challenges for efficient Mixed Reality system is to ensure the spatiotemporal coherence of the augmented scene between the virtual and the real objects. The quality of the Real/Virtual registration depends mainly on the accuracy of the 3D camera pose estimation. The goal of this chapter is to provide an overview on the recent multi-sensor fusion approaches used in Mixed Reality systems for the 3D camera tracking. We describe the main sensors used in those approaches and we detail the issues surrounding their use (calibration process, fusion strategies, etc.). We include the description of some Mixed Reality techniques developed these last years and which use multi-sensor technology. Finally, we highlight new directions and open problems in this research field.

Innovative ICT Applications in Transport and Logistics

This chapter examines the applications of innovative information and communication technology (ICT) applications in transport and logistics in Asia. Specifically, the authors examine two case studies of how a Logistics Service Provider (LSP), and a public sector agency based in Asia who acts as a regulator of ports and maritime services and facilities have effectively used ICT tools and applications to their advantage and how this has shaped the innovation landscape of the supply chain in Asia.

Towards a Rwandan NSDI

Awareness of the importance of spatial data in achieving development strategies is high in Rwanda. Government and non-governmental institutions are aspiring to use Geographic Information Technologies (GITs) in their day-to-day activities. The non-existence of a National Spatial Data Infrastructure (NSDI) in Rwanda brings to light serious issues for consideration. Still lacking is a spatial data policy relating to spatial data use. A mechanism to ease spatial data access and sharing is imperative. This paper describes SDI related efforts in Rwanda in a bid to establish the NSDI. Employing a multi-stakeholder approach to drive the process is advocated. To support this, SDI models in some countries are presented that could be applicable to the Rwandan context. Key players with potential roles in the NSDI were identified.

Estimating Fractional Snow Cover in Mountain Environments with Fuzzy Classification

The disproportionate amount of water runoff from mountains to surrounding arid and semiarid lands has generated much research in snow water equivalent (SWE) modeling. A primary input in SWE models is snow covered area (SCA) which is generally obtained via satellite imagery. Mixed pixels in alpine snow studies complicate SCA measurements and can reduce accuracy. A simple method was developed to estimate fractional snow cover using freely available Landsat and data derived from DEMs, commercial and free software, as well as fuzzy classification and recursive partitioning. The authors attempted to develop a cost effective technique for estimating fractional snow cover for resource and recreation managers confined by limited budgets and resources. Results indicated that the method was non-sensitive (P = 0.426) to differences in leaf area index and solar radiation between 4 March 2000 and 13 March 2003. Fractional snow cover was predicted consistently despite variation in model parameters between years, indicating that the developed method may be a viable way for monitoring fractional snow cover in mountainous areas where capital and resources are limited.

Spatial Model Approach for Deforestation

Java is very densely populated since it is inhabited by more than 60% of the total population of Indonesia. Based on data from the Ministry of Forestry, forest loss between 2000-2005 in Java was about 800,000 hectares. Regardless of the debate on whether the different methodologies of forest inventory applied in 2005 have resulted in an underestimation of the figure of forest loss or not, the decrease of forest cover in Java is obvious and needs immediate response. Spatial modeling of the deforestation will assist the policy makers in understanding this process and in taking it into consideration, when decisions are made on the issue. Moreover, the results can be used as data input to solve environmental problems resulting from deforestation. The authors of this chapter modeled the deforestation in Java by using logistic regression. Percentage of deforested area was considered as the response variable, whilst biophysical and socioeconomic factors, that explain the current spatial pattern in deforestation, were assigned as explanatory variables. Furthermore, the authors predicted the future deforestation process, and then, for the case of Java, it was validated with the actual deforestation derived from MODIS satellite imageries from 2000 to 2008. Results of the study showed that the impacts of population density, road density, and slope are significant. Population density and road density have negative impacts on deforestation, while slope has positive impact. Deforestation on Java Island tends to occur in remote areas with limited access, low density population and relatively steep slopes. Implication of the model is that the government should pay more attention to remote rural areas and develop good access to accelerate and create alternative non agricultural jobs in order to reduce pressure on the forest.

An Assessment of Several Taxonomies of Volunteered Geographic Information

User-Generated Content (UGC) in general, and Volunteered Geographical Information (VGI) in particular, are becoming more important as sources for official data bases, such as those used in national Spatial Data Infrastructures (SDIs). Discovering and assessing VGI as suitable geospatial resources for one’s purposes is hence becoming more important, but can be difficult. One way of assessing VGI resources is by classifying them into different types of resources, i.e. a taxonomy of resources. The question is whether such taxonomies can accurately identify suitable VGI resources. We assess five taxonomies both subjectively and using formal concept analysis to determine their discrimination adequacy, that is, how well the taxonomies discriminate between repositories containing UGC in general, or VGI in particular.

E-Planning through the Wisconsin Land Information Program

Through the lenses of Critical GIS and political economy, this paper examines the history of the Wisconsin Land Information Program (WLIP), which was created in 1989 and provides an early US example of the adoption of GIS at the local government level. Using a mixed methods approach and a case study design, the authors focus on the cooperation and conflicts among various actors and networks, at and between scales, during times of plentiful and lean resources. Catalyzed by the 1978 Larsen Report, the WLIP was unique in its inclusiveness of everyone involved in land records management. University academics brought together all the stakeholders to create a thematic and territorial network with political power and a unique funding mechanism. As land use planning and state budget deficits became prominent, the program became a target, leading to conflict and power struggles, particularly with the state Department of Administration (DOA). What began as an egalitarian, grass-roots, socially just, forward-thinking program has shape-shifted, and while the WLIP is still a viable and functioning program, its egalitarian goals have been subverted by economics.

The Role of GIS in City Competitiveness

Virtually every city and region is engaged in activities to improve their relative global competitiveness. The Geographic Information System (GIS) is one of the powerful tools of information storage and information access, providing spatial data to different stakeholders and cities across the world. This chapter will highlight the role of GIS technology in empirical assessment of the competition among cities or regions, using a variety of data assembled by many different individuals, businesses, and institutions. This valuable information can be used in decision-making by stakeholders who are taking part in the competition and can be disseminated, accessed, and updated in a dynamic way. This chapter discusses the origins of urban competitiveness, dynamics and functions of competition, and current and future research possibilities made possible by GIS.

A Neural Network for Modeling Multicategorical Parcel Use Change

This paper presents an artificial neural network (ANN) for modeling multicategorical land use changes. Compared to conventional statistical models and cellular automata models, ANNs have both the architecture appropriate for addressing complex problems and the power for spatio-temporal prediction. The model consists of two layers with multiple input and output units. Bayesian regularization was used for network training in order to select an optimal model that avoids over-fitting problem. When trained and applied to predict changes in parcel use in a coastal county from 1990 to 2008, the ANN model performed well as measured by high prediction accuracy (82.0-98.5%) and high Kappa coefficient (81.4-97.5%) with only slight variation across five different land use categories. ANN also outperformed the benchmark multinomial logistic regression by average 17.5 percentage points in categorical accuracy and by 9.2 percentage points in overall accuracy. The authors used the ANN model to predict future parcel use change from 2007 to 2030.

GIS Application of Healthcare Data for Advancing Epidemiological Studies

Healthcare practices increasingly rely on advanced technologies to improve analysis capabilities for decision making. In particular, spatial epidemiological approach to healthcare studies provides significant insight in evaluating health intervention and decisions through Geographic Information Systems (GIS) applications. This chapter illustrates a space-time cluster analysis using Kulldorff’s Scan Statistics (1999), local indicators of spatial autocorrelation, and local G-statistics involving routine clinical service data as part of a limited data set collected by a Northeast Ohio healthcare organization over a period 1994 – 2006. The objective is to find excess space and space-time variations of lung cancer and to identify potential monitoring and healthcare management capabilities. The results were compared with earlier research (Tyczynski & Berkel, 2005); similarities were noted in patient demographics for the targeted study area. The findings also provide evidence that diagnosis data collected as a result of rendered health services can be used in detecting potential disease patterns and/or utilization patterns, with the overall objective of improving health outcomes.