One of the more important elements of spatial information infrastructure is the organisational structure defining the obligations and dependencies between stakeholders that are responsible for the infrastructure. Many SDI practitioners and theoreticians emphasise that its influence on the success or failure of activities undertaken is significantly greater than that of technical aspects. Being aware of the role of the organisational structure in the creating, operating and maintenance of spatial information infrastructure (SII), Polish legislators placed appropriate regulations in the Spatial Information Infrastructure Act, being the transposition of the INSPIRE Directive into Polish Law. The principal spatial information infrastructure stakeholders are discussed in the article and also the scope of cooperation between them. The tasks and relationships between stakeholders are illustrated in UML, in both the use case and the class diagram. Mentioned also are the main problems and obstructions resulting from imprecise legal regulations.

The paper is organized into two sections. The first sets the background for data integration, and identifies some of the key issues that need to be considered. The second describes possible solutions of problems connected with the data integration. The steps of integrating data coming from different databases, datasets and data files are described in order to create a seamless geodatabase.

Population data are generally provided by state census organisations at the pre- defined census enumeration units. However, these datasets very are often required at user- defined spatial units that differ from the census output levels. A number of population estimation techniques have been developed to address these problems. This article is one of those attempts aimed at improving county level population estimates by using spatial disaggregation models with support of buildings characteristic, derived from national topographic database, and average area of a flat. The experimental gridded population surface was created for Opatów county, sparsely populated rural region located in Central Poland. The method relies on geolocation of population counts in buildings, taking into account the building volume and structural building type and then aggregation the people total in 1 km quadrilateral grid. The overall quality of population distribution surface expressed by the mean of RMSE equals 9 persons, and the MAE equals 0.01. We also discovered that nearly 20% of total county area is unpopulated and 80% of people lived on 33% of the county territory.

Polish spatial data infrastructure dates back 2010, the year when the Spatial Information Infrastructure Act transposing INSPIRE Directive entered into force. The present study provides valuable insight into the current status of Polish spatial data infrastructure (PSDI) as well as lessons learnt from so far efforts in implementing the principles and provisions of the INSPIRE Directive. Particular respect is given to policy, interoperability of data as well as cooperation between actors involved in PSDI establishment and maintenance. Data managed by the Surveyor General (SG), perceived as a backbone of a spatial data infrastructure, are of special importance. Finally, some conclusions and recommendations for further developments are given to foster SDI implementation in Poland. Results of the analysis clearly show that Polish spatial data infrastructure is in line with INSPIRE, and in a half of way being fully operational.

This article presents an overview of land use mapping work carried out in Poland. The authors discuss major early 20th-century publications and then review projects undertaken since WWII, pointing out the use of satellite imagery as a new source of data for land use mapping. They also discuss contemporary land use mapping programs pursued by international organizations in Europe, including in Poland. The outcome of work performed in Poland under the COR1NE Land Cover program is presented, and also the Land Cover Classification System developed by the FAO for the purposes of land use mapping is discusse

The purpose of this article was to provide the user with information about the number of buildings in the analyzed OpenStreetMap (OSM) dataset in the form of data completeness indicators, namely the standard OSM building areal completeness index (C Index), the numerical completeness index (COUNT Index) and OSM building location accuracy index (TP Index). The official Polish vector database BDOT10k (Database of Topographic Objects) was designated as the reference dataset. Analyses were carried out for Piaseczno County in Poland, differentiated by land cover structure and urbanization level. The results were presented in the form of a bivariate choropleth map with an individually selected class interval suitable for the statistical distribution of the analyzed data. The results confirm that the completeness of OSM buildings close to 100% was obtained mainly in built-up areas. Areas with a commission of OSM buildings were distinguished in terms of area and number of buildings. Lower values of completeness rates were observed in less urbanized areas. The developed methodology for assessing the quality of OSM building data and visualizing the quality results to assist the user in selecting a dataset is universal and can be applied to any OSM polygon features, as well as for peer review of other spatial datasets of comparable thematic scope and detail.