Revitalization is a set of urban and planning activities, coordinated by local self-government administration, aim of which is a social, architectural, planning and economical advantageous transformation of the specific area of the municipality, being in a state of crisis resulting from economic and social factors. Whereas, the renewal of the countryside is understood as shaping the living conditions of people in rural areas, with the local community as an animator and subject. The combination of revitalization and renewal of rural areas is a particularly important and urgent task, connected with rural and architectural chaos, decline of cultural context and local tradition. The aim of the article is to examine information needs for the renewal and revitalization of rural areas, and the possibilities of satisfying these needs using available spatial information systems. The first part of the paper contains an analysis of information needs in the field of renewal and revitalization of rural areas, created on the basis of literature and valid legal provisions. The created information resource illustrates the scope of data necessary for proper planning of renewal and revitalization of rural areas. In the second part, specific information needs were compared with the capabilities of existing spatial information systems. The possibility of obtaining free of expense information necessary for renewal and revitalization of rural areas was analysed. Systems which may be useful in the implementation of related activities are also indicated.

The main purpose of data mining in private and public sector institutions is to process and analyse data with the aim of generating reliable information for decision-making. Decision-making performance is determined by the availability of the relevant data and the user’s ability to adapt that data for analytical purposes. The popularity of spatial statistical tools is on the rise owing to the complexity of the analysed factors, their variation over time and their correlations with the spatial structure. Popular models should be applied in demographic analyses for the needs of the spatial planning process. The availability of high-resolution data and accurate analytical tools enhances the value of spatial analyses, and the described models can be universally applied to support the decision-making process. The aim of this study was to present the applicability of selected spatial statistical models for analysing demographic data in the planning process and to identify the main advantages of these models.

For photogrammetric works, a fundamental issue is the determination of camera internal orientation parameters (IOP). Without camera calibration, it is difficult to imagine a correct adjustment of the image network. Many industries use nonmetric cameras, ranging from automatics and robotics, to heritage inventories, and the increasingly popular social mapping phenomenon uses low-budget cameras. Many different calibration methods exist, but dedicated calibration fields are commonly replaced by fast in-plane calibration with regular patterns. The main goal of this research is to verify the thesis that calibrating cameras on a checkerboard gives worse results in determining IOP than on a laboratory test field which may translate into the resulting model. For the purpose of this study, a special field was constructed, allowing calibration of the instruments on the basis of the network solution by the bundle adjustment. Unlike classical 2D fields, the field is equipped with a cork background providing a good base for matching and automatically detecting measurement marks. Calibration results were compared with calibration performed on a checkerboard implemented in MATLAB Camera Calibration Toolbox. In order to determine IOP in MATLAB, images of the checkerboard must be taken in such a way, that the whole pattern fits into the frame, otherwise toolbox defines the incorrectly coordinate system, which has a bad impact on calibration results. Moreover, the determined parameters have several times larger standard deviations than those determined in the laboratory test field, which confirms the thesis.