Augmented reality (AR) is considered as one of the most significant technologies in the field of computer graphics and is utilised in many applications. In this chapter, we have presented a brief comprehensive survey of cultural heritage using augmented reality systems. This survey describes the main objectives and characteristics of marker-less augmented reality systems through presenting up-to-date research results in this area. We describe the marker-less technologies in the area of AR, indoor marker-less AR, outdoor marker-less AR, real-time solutions to the tracking problem, real-time registration, cultural heritage in AR, 3D remonstration techniques, as well as presenting the problems in each research.

In this paper we investigate the use of games technologies for the research and the development of 3D representations of real environments captured from GIS information and open source map data. Challenges involved in this area concern the large data-sets to be dealt with. Some existing map data include errors and are not complete, which makes the generation of realistic and accurate 3D environments problematic. The domain of application of our work is crisis management which requires very accurate GIS or map information. We believe the use of creating a 3D virtual environment using real map data whilst correcting and completing the missing data, improves the quality and performance of crisis management decision support system to provide a more natural and intuitive interface for crisis managers. Consequently, we present a case study into issues related to combining multiple large datasets to create an accurate representation of a novel, multi-layered, hybrid real-world maps. The hybrid map generation combines LiDAR, Ordnance Survey, and OpenStreetMap data to generate 3D cities spanning 1 km². Evaluation of initial visualised scenes is presented. Initial tests consist of a 1 km² landscape map containing up to 16 million vertices’ and run at an optimal 51.66 frames per-second.

Visualising complex data facilitates a more comprehensive stage for conveying knowledge. Within the medical data domain, there is an increasing requirement for valuable and accurate information. Patients need to be confident that their data is being stored safely and securely. As such, it is now becoming necessary to visualise data patterns and trends in real-time to identify erratic and anomalous network access behaviours. In this paper, an investigation into modelling data flow within healthcare infrastructures is presented; where a dataset from a Liverpool-based (UK) hospital is employed for the case study. Specifically, a visualisation of transmission control protocol (TCP) socket connections is put forward, as an investigation into the data complexity and user interaction events within healthcare networks. In addition, a filtering algorithm is proposed for noise reduction in the TCP dataset. Positive results from using this algorithm are apparent on visual inspection, where noise is reduced by up to 89.84%.

Crisis management is a modern phenomenon brought about by natural disasters and acts of terrorism. Building a modern crisis management response program needs a multi-disciplinary architecture and accurate, up-to-date, real-world data. The creation of virtual environments depicting critical infrastructure buildings and conduits between these highly interconnected man-made structures is a complex procedure. The crossover between games technology and use of real-world map data for real-world simulations is becoming more common with the advancements of computer hardware and software, and the accuracy of real-world map data. However, there are many problems with using real-world map data for simulation due to the large potential of missing and error prone data involved in this big data. Within this work we use three types of data sets; Ordnance Survey data, LiDAR data, and OpenStreetMap data to provide accurate map and 3D environment information for crisis management systems. Combining these large data-sets can reduce errors and retrieve missing data for use within a modern game engine for visualization analysis. We propose a novel technique for data extraction using adjacency matrices for custom model generation corresponding to real-world structures such as landscapes, buildings, road systems, area boundaries, or a combination of these at different resolutions.