Automated recognition of regular, radial and ring structures in the configuration of a street-road network of settlements

DOI: 10.35595/2414-9179-2020-1-26-410-420

View or download the article (Rus)

About the Authors

Anastasia A. Shurygina

Moscow State University named after M.V. Lomonosov,
Leninskie Gory, 1, 119991, Moscow, Russia;
E-mail: shur.a17@yandex.ru

Timofey E. Samsonov

Moscow State University named after M.V. Lomonosov,
Leninskie Gory, 1, 119991, Moscow, Russia;
E-mail: tsamsonov@geogr.msu.ru

Abstract

The article describes the experience of applying various approaches to the recognition of some of the most common settlements planning structures, which include radial, regular and ring elements. At the first stage, it is necessary to identify typical properties and elements of the corresponding figures, then find a way to automate the search for such entities in an arbitrary road network of a settlement. To solve the second problem, well-known algorithms are used that are associated with the analysis of the uniformity of the shape of neighborhoods of a settlement. Modifications of the algorithm for searching for radial structural elements are proposed, and the author’s method of detecting ring elements is tested.

The selected approaches are implemented in the form of a script in the Python programming language, which sequentially checks the road network given to the input for the presence of lattice or radial-ring planning structure elements. The technique was tested in fifty cities of the world. Verification of the results was carried out by comparing the response of the algorithm with the expert opinion of the research authors or literature on the topic. The accuracy of the classification was 80 %. The algorithm successfully coped with the reference examples of layouts, but experienced difficulties with their modifications, associated, for example, with the lack of closure of the ring elements of the radial-ring structure.

The results can be demanded in problems of cartographic generalization, which require recognition and preservation of typical features of spatial objects.

Keywords

pattern recognition, planning structures, graph theory

References

  1. Costea D., Leordeanu M. Aerial image geolocalization from recognition and matching of roads and intersections. ArXiv:1605.08323. Computer Science. BMVC, 2016. DOI: 10.5244/c.30.118.
  2. Heinzle F., Anders K.-H., Sester M. Graph based approaches for recognition of patterns and implicit information in road networks. Proceedings of the 22nd International Cartographic Conference, A Coruña, 2005. DOI: 10.1007/978-3-642-19143-5_24.
  3. Labutina I.A. Aerospace imagery. Moscow: ASPECT PRESS, 2004. 184 p. (in Russian).
  4. Lappo G.M. Geography of cities: Textbook for geographic faculties of universities. Moscow: Humanitarian Publishing Center VLADOS, 1997. 480 p. (in Russian).
  5. Li W., Goodchild M.F., Church R. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. International Journal of Geographical Information Science, 2013. No 27 (6). P. 1227–1250. DOI: 10.1080/13658816.2012.752093.
  6. Marshall S. Streets and patterns. Routledge, 2004. 336 p.
  7. Newman M. Networks. Oxford: Oxford University Press, 2018. 800 p.
  8. Pertsik E.N. Urban geography: Textbook. Moscow: Academy, 2009. 432 p. (in Russian).
  9. Schemala D., Schlesinger D., Winkler P., Herold H., Meinel G. Semantic segmentation of settlement patterns in gray-scale map images using RF and CRF within an HPC environment. GEOBIA 2016: Solutions and Synergies. University of Twente, Faculty of Geo-Information and Earth Observation (ITC), 2016. DOI: 10.3990/2.420.
  10. Shurygina A.A., Samsonov T.E. Analisys and systematization of morphometric characteristics of major classes of geographic maps’ objects. Scientific research of young scientists-cartographers performed under the guidance of the staff of the Department of Cartography and Geoinformatics of the Geographical Faculty of MSU named after M.V. Lomonosov. Moscow: KDU, 2017. Р. 110–121 (in Russian).
  11. Stoter J., Burghardt D., Duchêne C., Baella B., Bakker N., Blok C., Pla M., Regnauld N., Touya G., Schmid St. Methodology for evaluating automated map generalization in commercial software. Computers, Environment and Urban Systems, 2009. V. 33. No 5. P. 311–324. DOI: 11.1016/j.compenvurbsys.2009.06.002.
  12. Tian J., Song Z., Gao F., Zhao F. Grid pattern recognition in road networks using the C4.5 algorithm. Cartography and Geographic Information Science, 2016. V. 43. Iss. 3. P. 266–282. DOI: 10.1080/15230406.2015.1062425.
  13. Wieland M., Pittore M. Large-area settlement pattern recognition from Landsat-8 data. ISPRS Journal of Photogrammetry and Remote Sensing, 2016. V. 119. P. 294–308. DOI: 10.1016/j.isprsjprs.2016.06.010.
  14. Yargina Z.N., Kositsky Y.V., Vladimirov V.V., Gutnov A.E., Mikulin E.M. Fundamentals of the theory of urban planning. Textbook for architectural specialties of universities. Moscow: Stroyizdat, 1986. P. 65–75 (in Russian).
  15. Zahn C.T. Graph-theoretical methods for detecting and describing gestalt clusters. IEEE Transactions on Computers, 1971. V. 100. No 1. P. 68–86. DOI: 10.1109/T-C.1971.223083.

For citation: Shurygina A.A., Samsonov T.E. Automated recognition of regular, radial and ring structures in the configuration of a street-road network of settlements. InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: Moscow University Press, 2020. V. 26. Part 1. P. 410–420. DOI: 10.35595/2414-9179-2020-1-26-410-420 (in Russian)