METHOD OF MOVING OBJECT POSITIONING WITHOUT USING GLOBAL GEO-REFERENCED DATA

The main problem of decision making in emergency situations is the reliability of these decisions. Emergencysituations by virtue of its unpredictable and dynamic nature often have incomplete and inaccurate information.The use of accumulated experience allows to find reliable solutions based on known precedents ofemergency situations. Geographic information systems (GIS) can act as a tool for accumulating experience andgenerating solutions based on it. The cartographic basis of GIS allows analyzing emergency situations, takinginto account their spatial and temporal characteristics. However, the cartographic representation of precedentswith adopted solutions describes them too narrowly. There is no idea what properties of the situation are significantand whether the precedent solution can be applied in other circumstances. The use of known images andtheir admissible transformations, created on the basis of expert knowledge, can solve this problem. The imagegeneralizes a set of similar precedents. The purpose of such generalization is to expand the area of applicationof information from private observations by determining the boundaries of permissible transformations. However,the need to attract experts for their creation is a difficult task, since each situation is unique in its own way.No less problematic is the transfer of experience from one spatial and temporal domain to another. In this paperwe consider an approach to automatic image generation. We propose a method of creating a geoinformationmodel of emergency situations, which includes the generalization of precedents on a common location. Thisapproach is aimed at improving the reliability of prediction of emergency situations. An experiment was conductedto synthesize images based on precedents of road accidents and evaluate their effectiveness compared toindividual precedents. The use of the developed method of automatic data processing to create images is relevant,as it significantly reduces the cost of knowledge acquisition. The use of spatial generalizations also eliminatesthe need for expert knowledge, since the formation of precedent sets is performed by analyzing their geographicallocation.