Integration of GIS and video surveillance

This paper discusses the integration of three-dimensional (3D) geographic information systems (GIS) and video surveillance systems using augmented reality (AR) techniques. The motivation for this integration is to overcome problems faced by conventional video surveillance systems. Explicit information concerning which camera currently monitors what area in such systems is missing; therefore, insight into the situation depends heavily on the operator’s training and experience. To ensure the complete coordination and monitoring of a situation in a system with multiple cameras, it is necessary to introduce a single reference system. GIS arises as a natural solution because it not only provides a solid ground truth but also provides semantic information that can be highly important in certain video surveillance applications. To integrate information into a GIS application, that information must be georeferenced. Based on our previous research regarding the addition of georeferencing information to surveillance video, this paper introduces models that can be applied to help integrate video and GIS. With an analogy to Milgram’s continuum between the real world and virtual reality, and analogous to the augmented reality and augmented virtuality in Milgram’s continuum, two models of integration are defined here: GIS-augmented video and video-augmented GIS. Then, we define the architecture of GIS-based video surveillance based on these proposed integration models, and finally, a prototype is implemented. The implemented prototype serves as a basis for analysing possible applications of real-world systems based on the integration of GIS and video.     

Spatial Modeling of Geometallurgical Properties: Techniques and a Case Study

High-resolution spatial numerical models of metallurgical properties constrained by geological controls and more extensively by measured grade and geomechanical properties constitute an important part of geometallurgy. Geostatistical and other numerical techniques are adapted and developed to construct these high-resolution models accounting for all available data. Important issues that must be addressed include unequal sampling of the metallurgical properties versus grade assays, measurements at different scale, and complex nonlinear averaging of many metallurgical parameters. This paper establishes techniques to address each of these issues with the required implementation details and also demonstrates geometallurgical mineral deposit characterization for a copper–molybdenum deposit in South America. High-resolution models of grades and comminution indices are constructed, checked, and are rigorously validated. The workflow demonstrated in this case study is applicable to many other deposit types.     

Removal of heavy metal ions from drinking water by alginate-immobilised <Emphasis Type="Italic">Chlorella sorokiniana</Emphasis>

This paper investigates the potential of alginate-immobilised Chlorella sorokiniana for removing Cu²⁺, Ni²⁺ and Cd²⁺ ions from drinking water solutions. The effects of initial metal concentrations, contact times and temperatures on the biosorptions and removal efficiencies of the tested metals were investigated at initial pH values of 5, and pH effects were studied within the range of 3–7. When studying the effects of initial metal concentrations, the highest experimental removal yields achieved for Cu²⁺, Ni²⁺ and Cd²⁺ ions were 97.10, 50.94 and 64.61 %, respectively. The maximum biosorption capacities obtained by the Langmuir isotherm model for the biosorptions of Cu²⁺, Ni²⁺ and Cd²⁺ ions by alginate-immobilised C. sorokiniana were found to be 179.90, 86.49 and 164.50 mg/g biosorbent, respectively. The experimental data followed pseudo-second-order kinetics. At an initial metal concentration of 25 mg/L, immobilised algae could be used in at least 5 successive biosorption–desorption cycles. SEM and EDS analyses revealed that the metals bonded to the biosorbent. Bi- and multi-metal systems of Cu²⁺, Ni²⁺ and Cd²⁺ were investigated at initial metal concentrations of 30, 50 and 100 mg/L. The removal of Cd²⁺ as well as Ni²⁺ in such systems was negatively affected by the presence of Cu²⁺. The removal efficiency for Cu²⁺ in multi-metal systems decreased by 5–7 %, whilst in the cases of Cd²⁺ and Ni²⁺ the efficiencies decreased by up to 30 %. Nevertheless, the results obtained show that alginate-immobilised C. sorokiniana can efficiently remove the metals tested from polluted drinking water sources.