The Pilsen-Senec Mesolithic station is situated on the left banks of the Berounka River, 14-16 meters above its current level, in an industinct saddle 326 m above sea level. The chipped stone industry form Pilse-Senec has a distinctly diverse material composition. The raw material are predominantly local and include different varieties of hornstone, lydite, slate, quartz, sandstone and limonite. Imported materials are represented by Nordic flint, differnet varieties of Bavarian hornstone and northwest Bohemian quartzite (Bečov, Skršín and Tušimeice types). The cipped stone industry from Pilsen-Senec is a relatively large collection of artifacts (2 069 pieces) with madny different raw materials. Trhe pridution part of the collection is absolutely predominat. The number of microliths and retouched artifats is very small. Hammerstones,anvils and heating stones are also presnet. Covetional typological analysis has differentiated between two groups of artifacts. The first group has characteristic Mesolithic microliths - such as Komornica type points, sements, a triangle, Borki type blade, and microblades. A tendecy toward microlithization is also indicated by several other tools such as end-scrapers, burins and awls. The second group consists of artifacts typical for the late Paleolithic period, for example a convexed backed point, chisels, awls,, bacek blades, lateral burigns and long blades. Using the typical approach, it would seem appropriate to divide the complex itno twoo cultural complexes- late Paleolithic and Mesolithic., Jan Fridrich, Ivana Fridrichová-Sýkorová, Milan Metlička., and Obsahuje seznam literatury
Presented in this paper is the idea of GIS layers semantic recognition methodology. The aim was to evaluate a possibility of GIS layer recognition based on spatial analysis and performance tests which validate proposed methodology. The final interest was to develop a GIS layer classifier and evaluate its function for independent data set. In my approach to the classification of the GIS data layers I use methods based on the nearest neighbor and histogram of the distance matrix. The reasons for such a solution are in good complexity of the spatial data description and in implementation of these algorithms under statistics software. In the range of the experiment tests I developed methodology for classification and I verified that it is possible to recognize the spatial layer via spatial statistic. Then I developed the classifier based on the Kohonen's Self Organization Maps and evaluated it on a test set. All the executed tests under artificial spatial data and real GIS data show that the proposed methodology is fully relevant and forms a basis for successful use in practical applications. All executed classification models showed that the proposal methodology could directly recognize the GIS layer, as for layers with similar spatial characteristic they recognize only a class of layers. For complete recognition it is necessary to add other information about layers.