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Mileva Samardžić‐Petrović Suzana Dragićević Miloš Kovačević Branislav Bajat 《Transactions in GIS》2016,20(5):718-734
Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters. 相似文献
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Aleksandar Milosavljević Dejan Rančić Aleksandar Dimitrijević Bratislav Predić Vladan Mihajlović 《International journal of geographical information science》2016,30(10):2089-2107
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. 相似文献
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A. Petrovič M. Simonič 《International Journal of Environmental Science and Technology》2016,13(7):1761-1780
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. 相似文献
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