Algorithm of automatic digital cartographic generalisation with the use of contractive self-mapping

• Autorzy: Banasik Piotr , Chrobak Tadeusz , Biegun Bartosz

Identyfikatory

DOI

10.2478/pcr-2022-0001

• Języki publikacji: EN

Abstrakty

EN

The research of modern cartography in the field of digital generalisation focuses on the development of such methods that would be fully automatic and give an unambiguously objective result. Devising them requires specific standards as well as unique and verifiable algorithms. In metric space, a proposal for such a method, based on contractive mapping, the Lipschitz and Cauchy conditions and the Banach theorem, using the Salishchev metric, was presented in the publication (Barańska et al., 2021). The method formulated there is dedicated to linear objects (polylines). The current work is a practical supplement to it. It presents the practical implementation of the algorithm for automatic and objective generalisation. The article describes an operational diagram of the subsequent stages of the proposed generalisation method. In the test example, a binary tree structure of an ordered polyline was created. It was simplified in two selected scales and its shape after generalisation was illustrated. The resulting polyline obtained by the fully automatic method was verified in terms of accuracy.

Słowa kluczowe

EN

digital generalisation; contractive self-mapping; minimum dimensions of Salishchev; polyline of binary tree structure; segmented line; contraction triangles

• Wydawca: Polskie Towarzystwo Geograficzne. Oddział Kartograficzny
• Czasopismo: Polish Cartographical Review
• Rocznik: 2022
• Tom: Vol. 54, No. 1
• Strony: 1--10
• Opis fizyczny: Bibliogr. 10 poz., rys., tab.

Twórcy

autor

Banasik Piotr

• AGH University of Science and Technology Accepted: 17.03.2022 Kraków, Poland

• https://orcid.org/0000-0002-3604-4019

autor

Chrobak Tadeusz

• Polish Academy of Arts and Sciences Kraków, Poland

• https://orcid.org/0000-0002-4614-3432

autor

Biegun Bartosz

• Student AGH, University of Science and Technology

Bibliografia

• Barańska, A., Bac-Bronowicz, J., Dejniak, D., Lewiński, S., Krawczyk, A., Chrobak, T. (2021). A Unified Methodology for the Generalisation of the Geometry of Features. SPRS International Journal of Geo-Information, 10 (3), art. no. 107, 1-25. https://doi.org/10.3390/ijgi10030107
• Blum-Krzywicka, E. (2017). Elements of Maps Contents with (0D) Point Reference Units. In: Map Functions; Springer Geography Switzerland: Cham, Switzerland, 41-84. TWO:10.1007/978-3-319-47358-1_2
• Chrobak, T. (2010). The role of least image dimensions in generalized of object in spatial databases. Geodesy and Cartography, 59, 99-120, DOI: 10.2478/v10277-012-0004-y
• Chrobak, T., Szombara, S. Kozioł, K., Lupa, M. (2017). A method for assessing generalized data accuracy with linear object resolution verification. Geocarto International. 32, 238-256. https://doi.org/10.1080/10106049.2015.1133721
• Chrobak, T., Lupa, M., Szombara, S., Dejniak, D. (2019). The use of cartographic control points in the harmonization and revision of MRDBs. Geocarto International, 34, 260-275. https://doi.org/10.1080/10106049.2017.1386721
• Courtial, A., El Ayedi, A., Touya, G., Zhang, X. (2020). Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation. ISPRS Intenrnational Journal of Geo-Information, 9(5), 338. https://doi.org/10.3390/ijgi9050338
• Directive, (2007). https://eurlex.europa.eu/LexUriServ/LexUriServ.do?uri=CONSLEG:2007L0002: 20070515:PL:HTML
• Kronenfeld, B., Buttenfield, B., Stanislawski, L. Map generalisation for the Future. ISPRS Intenrnational Journal of Geo-Information, 9(8), 468. https://doi.org/10.3390/ijgi9080468
• Salishchev, K. (2003). Kartografia ogólna. Wydawnictwo Naukowe PWN: Warszawa, Poland.
• Sydow E. (1866). Drei Karten-Klippen - Geo-kartographische Betrachtung. Geographisches Jahrbuch, 1, 348-361.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)