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1

Przełamywanie barier przy wdrażaniu dyrektywy INSPIRE

Iwaniak A., Kubik T., Netzel P., Paluszyński W.

Roczniki Geomatyki

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2010

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T. 8, z. 1

63-72

PL

W niniejszej pracy dokonano przeglądu różnorodnych zagadnień, które mają wpływ na tempo wdrażania dyrektywy INSPIRE. Zakres przeglądu objął trzy obszary, odpowiadające zakresom stosowania pojęcia interoperacyjności, a mianowicie: obszar techniczny, semantyczny i biznesowy.

EN

The paper presents a review of issues that influence the implementation of the INSPIRE Directive. The scope of the review covers three areas connected with the notion of interoperability, namely: a) technical area (metadata, presentation, copyrights for services and data, modeling and coding, testing, technological solutions), b) semantic area, c) business area. In the conclusions, factors are indicated which, in the authors. opinion, hamper the implementation of the INPIRE Directive in Poland; drawing attention to: lack of appropriate models for financing activities connected with the spatial information infrastructure, delays in issue of guidelines for elaboration of metadata and insufficent popularization of free software in public administration.

2

Rola geoinformacji w rozwoju społeczeństwa informacyjnego

Paluszyński W.

Roczniki Geomatyki

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2007

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T. 5, z. 6

125-131

EN

The development of the idea of information society is connected with many fields of activity. Geoinformation is one of them. Its dynamic development is related to the usage of information technology in obtaining, processing, analyzing and making accessible the geographical information. The key elements contributing to the development of information society and their relation to the development of geoinformation are discussed in this paper. Particularly, the INSPIRE Directive is of great importance in this process. The author also presents practical examples of geoinformation usages in the information society processes.

3

Feature extraction in high-resolution raster images using neural networks

Iwaniak A., Kubik T., Paluszyński W., Tymków P.

Reports on Geodesy

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2006

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z. 2/77

263-271

4

Generalizacja danych topograficznych o szczegółowośći 1:10 000 do skali 1:50 000. Cz. 2

Iwaniak A., Chybicka I., Ostrowski W., Paluszyński W.

Polski Przegląd Kartograficzny

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2004

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T. 36, nr 4

266--273

PL

W pierwszej części artykułu omówiono znaczenie generalizacji w procesie budowy Krajowego Systemu Informacji Geograficznej oraz teoretyczne zasady generalizacji sieci dróg i zabudowy dla skali 1:50 000. W drugiej części opisano narzędzia informatyczne do automatyzacji procesu generalizacji oraz implementacje bazy wiedzy dotyczącej reguł generalizacji w środowisku programowym DynaGEN. Poprawność opracowanej bazy wiedzy zweryfikowano przez przeprowadzenie dwóch eksperymentów. Pierwszy eksperyment dotyczył generalizacji warstw tematycznych Bazy Danych Topograficznych (BDT) sieci transportowej i zabudowy w obrębie podwar-szawskiej miejscowości Łomianki. Drugi eksperyment polegał na generalizacji warstwy tematycznej sieci transportowej w okolicach Kowalewa Pomorskiego.

EN

The most advanced software for generalization of digital data available on the market today is that by Laser Scan. The company participated in the AGENT The most advanced software project, which was described in the first part of the article. What resulted from the project was Clarity, a very advanced map generalization system. What differentiated Laser Scan from then contemporary software, was the fact that it accounted for the contextuality of generalization, i.e. the same objects can be generalized differently depending on the objects surrounding them. It was possible because of application of a unique object technology, where each object could be generalized in many ways depending on the context. The DynaGEN system by Intergraph is an alternative generalization software. It is less expensive and has significantly more practical applications. It uses the same generalization algorithms, but cannot do alternative operations. Therefore one can either generalize small areas in an interactive way, or generalize automatically with later manual corrections. Similarly to Laser Scan, it requires software configuration and also the construction of knowledge base. The second part of the article presents an implementation of the knowledge base containing the rules of generalization in DynaGEN environment. The accuracy of the arranged knowledge base had been verified by two experiments. The first one involved generalization of thematic levels of Topographic Database of the road network and buildings within the town of Łomianki near Warsaw. The second one consisted in generalization of the road network in the area of Kowalewo Pomorskie in Northern Poland. The knowledge base prepared in DynaGEN system contained two sets of rules. The first one included the rules applied automatically and which, in limited sequence, are used for the initial preparation of data. The second set contained the rules describing basic types of interactive generalization processes supervised by a cartographer, which were vital for the application sequence. The generalization of a road network consists of two stages. The first one involves an analysis and initial data processing. Its range includes a construction of a hierarchical model of a road network and the connection of road segments within the whole area. The second, main stage of the generalization of a road network involves selection and simplification of paved roads, dirt roads and paths. Generalization of built-up areas is performed in one step, without initial data processing. The prepared knowledge data describes the process of generalization of topographic data from 1:10000 into 1:50 000. It provides universal material which can be implemented in various software environments.

5

Generalizacja danych topograficznych o szczegółowości 1:10 000 do skali 1:50 000

Iwaniak A., Ostrowski W., Paluszyński W.

Polski Przegląd Kartograficzny

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2004

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T. 36, nr 3

177-186

PL

W pierwszej części artykułu opisano i zilustrowano reguły generalizacji sieci drogowej i zabudowy przy opracowywaniu mapy topograficznej 1:50 000 na podstawie mapy 1: 10 000. W drugiej części artykułu zostanie zaprezentowana implementacja bazy wiedzy na podstawie zdefiniowanych reguł w środowisku programowym Dynagen oraz przykłady praktycznego wykorzystania tej implementacji do generalizacji Bazy Danych Topograficznych.

EN

Due to the development of geographic information systems, in the developed countries more than 70% of decisions in public administrations are taken basing on spatial data. Particular regions have their own databases with various levels of detail, usually corresponding to standard topographic scales (1:10 000, 1:25 000, 1:50 000 etc.). The question is whether it would be possible to keep just one, detailed topographic database and generate cartographic presentations in different scales from it, using automatic generalization of spatial data. This problem is particularly vital in Poland, because the National Geographic Information System (KSIG), which is being implemented at the moment, consists of, among other units, a Topographic Database (BDT) with the level of detail corresponding to the map in 1:10 000 and a Level 2 Vector Map (VMAP 2) with the level of detail corresponding to the scale of 1:50 000. Since 2002 the Faculty of Environmental Engineering and Geodesy of Agricultural University of Wroclaw together with the Warsaw University Chair of Cartography have been working on a research project "Automatization of the process of generalization of topographic maps from the scale of 1:10 000 into 1:50 000". In the first stage of the research attempts were made to elaborate a system of automatic genera-lization of road network and buildings with the use of DynaGen software. In the first part of the article the rules of generalization of these two components are presented and illustrated. Criteria and rules for the choice of streets and lower road categories are established (criteria of density, length, functionality and spatial relations). Generalization of buildings presented in the scale of 1:10 000 can be conducted in two ways: either with symbols of separate buildings or by replacing them with a symbol of a built-up area. In the first case we are dealing with the following types of generalizations: generalization of qualitative features, substitution of a collective symbol for separate symbols, choice of buildings, replacement of building outlines with symbols, building simplification, combining and changing of building location. In the second case separate objects (buildings) are replaced with a collective symbol (built-up area), the shape of which is further simplified, extended and combined.

6

Użycie sieci neuronowych do klasyfikacji obdszarów miejskich na zdjęciach satelitarnych

Iwaniak A., Krówczyńska M., Paluszyński W.

Acta Scientiarum Polonorum. Geodesia et Descriptio Terrarum

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2002

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Vol. 1, nr 1-2

5--13

PL

Niniejsza praca przedstawia podejście do procesu klasyfikacji obrazów satelitarnych alternatywne wobec istniejących metod. Do identyfikacji terenów miejskich zobrazowanych na zdjęciach satelitarnych zastosowana została sztuczna sieć neuronowa. W pracy wykorzystano zobrazowania wykonane przez satelitę Landsat skanerem Thematic Mapper oraz mapę pokrycia terenu opracowaną w programie CORINE Land Cover. Oceny wyników dokonano metodą porównania punktowego z mapą topograficzną. Badania zostały przeprowadzone na terenach testowych: Aglomeracja Warszawska oraz Krakowska.

EN

This work presents a new approach to satellite image classification process. An artificial neural network has been applied to identification of urban areas mapped in satellite images. The results presented here are based on images obtained from the Landsat satellite using the Thematic Mapper scanner and a land cover map produced under the CORINE Program. The evaluation of the results has been conducted using a point-topoint comparison with a topographic map. The test areas include the metropolitan areas of Warsaw and Cracow.

7

Generalizacja map numerycznych - koncepcje i narzędzia. Cz. 2

Iwaniak A., Paluszyński W., Żyszkowska W.

Polski Przegląd Kartograficzny

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1998

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T. 30, nr 3

163-172

PL

Artykuł jest przeglądem operatorów generalizacji zdefiniowanych w modelu McMaster i Shea (1992) oraz realizujących je algorytmów, zastosowanych w systemach komputerowych do generalizacji map. Opisano generalizację map numerycznych z wykorzystaniem modułu MGE MAP GENERALIZER oraz trudności związane z automatyzacją procesu generalizacji.

EN

The generalization operators are a useful paradigm for reoresenting the process of map generalization. Particularly popular is the set of generalization operators of the McMaster & Shea model of generalization, which was implemented, among others, in the MGE Map Generalizer system. The paper also describes these operators, the algorithms used to implement them, the parameters controlling their operation and examples of steps involved in generalizing numerical maps using this system. The paper further describes commonly used sequences of operators, used in the map generalization process. Finally, there is an overview of issues arising when trying to fully automate the generalization process.

8

Generalizacja map numerycznych - koncepcje i narzędzia. Cz. 1

Iwaniak A., Paluszyński W., Żyszkowska W.

Polski Przegląd Kartograficzny

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1998

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T. 30, nr 2

79-88

PL

W artykule omówiono najnowsze poglądy i podejścia teoretyczne do generalizacji komputerowej. Na tym tle przedstawiono modele tego procesu oraz stosowane obecnie algorytmy i operatory, stanowiące składniki wiedzy proceduralnej systemów komputerowych służące do generalizacji map.

EN

The paper presents selected issues in automatic generalization, particularly in producing medium-scale maps from large-scale maps. The first part describes the development of ideas and research directions in generalization, as well as selected models of generalization. Due to the subjective nature of generalization, directly applying the traditional (manual) techniques of map generalization to computer systems is not possible. It is nessesary to use artificial intelligence methods and to formulate the problems from the standpoints of computer systems (R.B. McMaster 1991), such as the operators and alorithms for generalization, and the correctness crriteria. So far the attemps at automatic generalization have been limited to imitating the traditional approach. These focused on the geometric aspects of generalization of the separate map elements, mainly lines and points. However, most algorithms simplify all lines in the same way, and the total result is not as good as good as what is achieved using traditional methods (W. Tobler 1964, D.H. Douglas and T.K. Peucker 1973, D.M. Brophy 1973). Also the 'root law' of F. Topfer (1979) used in selecting map elements, ignores the specific features of elements. Less of a problem is qualitative generalization, i.e. the process of grouping the low level features into a feature of higher level. But here, too, some fundamental understanding of the characteristics of the domain and the related classification methods, is required. Recently, many researches have been trying to construct a computer system for map generalization at various scales and map contents, that would consider both qualitative and quantitative aspects of generalization (B.G. Nickerson, K.S. Shea 1992). They are based on a holistic approach to generalization and consider the following three aspects: 1) generalization models as the theoretical foundation for digital generalization, 2) processes and operators for generalization, 3) cartographic knowledge, encoded as rules. The models of generalization define the components of the process and relations between them. Among the most important models, developed from the point of view of computer systems are the models of: B.G. Nickerson and H. Freeman (1986), R.B. McMaster and K.S. Shea (1992), K. Brassel and R. Weibel (1988) as well as B. Powitz (1990), presented in the first part of the paper. An important aspect of those models are the operators and generalization knowledge which will be discussed in the second part of the paper.