The article is devoted to the study of the possibility of using machine learning methods to solve the problem of classifying buildings and structures by their functional purpose based on geospatial data. The problem of determining the buildings and structure types in real conditions with limited initial data is outlined. Existing approaches to solving the problem of classifying objects are considered. A new dataset was created, which includes about 66 thousand objects of various functional affiliations in the territory of the Russian Federation. The stages of data preparation, feature extraction and the process of normalizing the objects’ geometries on the map are considered. Experiments were conducted using machine learning methods, including artificial intelligence methods. The research results show that the maximum classification accuracy using a graph neural network is 83%, which makes the proposed approach promising for practical applications in geographic information systems. A number of factors have been identified that reduce the classification accuracy associated with the insufficiency of geometric information and the shape details common for buildings of certain categories in real development conditions. Recommendations are given for improving the classification accuracy by optimizing the neural network architecture and expanding the feature set. Thus, the article proposes an effective approach to the automated classification of buildings and structures based on the analysis of geometric properties and the environment, which can significantly facilitate the processes of design and infrastructure management.
1. Chen W., Zhou Yu., Wu Q., Chen G., Huang X., Yu B. Urban Building Type Mapping Using Geospatial Data: A Case Study of Beijing, China. Remote Sensing. 2020;12(17). https://doi.org/10.3390/rs12172805
2. Gummidi S.R.Bh., Mao R., Lanau M., Liu G. Developing an Urban Resource Cadaster for Circular Economy. In: Circular Economy for Buildings and Infrastructure: Principles, Practices and Future Directions. Cham: Springer; 2024. P. 83–95. https://doi.org/10.1007/978-3-031-56241-9_6
3. Xie Ju., Zhou J. Classification of Urban Building Type from High Spatial Resolution Remote Sensing Imagery Using Extended MRS and Soft BP Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2017;10(8):3515–3528. https://doi.org/10.1109/JSTARS.2017.2686422
4. Li J., Huang X., Tu L., Zhang T., Wang L. A Review of Building Detection from Very High Resolution Optical Remote Sensing Images. GIScience & Remote Sensing. 2022;59(1):1199–1225. https://doi.org/10.1080/15481603.2022.2101727
5. Ivaschenko A., Golovnin O., Golovnina A., Dodonova E. An Assembled Model of Multilayer Geoinformation Space-Time. Informatics and Automation. 2025;24(2):684–711. (In Russ.). https://doi.org/10.15622/ia.24.2.12
6. Baranova I.V., Gilin S.V. Building Recognition Hybrid Algorithm for Satellite Images Based on the Beetle Method and the Area Exclusion Algorithm. Bulletin of the South Ural State University. Series: Computational Mathematics and Software Engineering. 2024;13(2):56–76. (In Russ.). https://doi.org/10.14529/cmse240204
7. Galeev D.T., Miroshnichenko S.Yu. Primenenie iskusstvennykh neironnykh setei dlya resheniya zadachi vydeleniya zdanii na aerokosmicheskikh izobrazheniyakh. In: Optiko-elektronnye pribory i ustroistva v sistemakh raspoznavaniya obrazov i obrabotki izobrazhenii. Raspoznavanie-2019: sbornik materialov XV Mezhdunarodnoi nauchno-tekhnicheskoi konferentsii, 14–17 May 2019, Kursk, Russia. Kursk: Southwest State University; 2019. P. 64–66. (In Russ.).
8. Hecht R., Meinel G., Buchroithner M. Automatic Identification of Building Types Based on Topographic Databases – A Comparison of Different Data Sources. International Journal of Cartography. 2015;1(1):18–31. https://doi.org/10.1080/23729333.2015.1055644
9. Lu X., Li H., Xu Yo., Liu J., Chen Zh. Measuring the Similarity Between Shapes of Buildings Using Graph Edit Distance. International Journal of Digital Earth. 2024;17(1). https://doi.org/10.1080/17538947.2024.2310749
10. Yan X., Ai T., Yang M., Yin H. A Graph Convolutional Neural Network for Classification of Building Patterns Using Spatial Vector Data. ISPRS Journal of Photogrammetry and Remote Sensing. 2019;150:259–273. https://doi.org/10.1016/j.isprsjprs.2019.02.010
11. Yan X., Ai T., Yang M., Tong X. Graph Convolutional Autoencoder Model for the Shape Coding and Cognition of Buildings in Maps. International Journal of Geographical Information Science. 2021;35(3):490–512. https://doi.org/10.1080/13658816.2020.1768260
12. Hu Ya., Liu Ch., Li Zh., Xu Ju., Han Zh., Guo J. Few-Shot Building Footprint Shape Classification with Relation Network. ISPRS International Journal of Geo-Information. 2022;11(5). https://doi.org/10.3390/ijgi11050311
13. Kang J., Körner M., Wang Yu., Taubenböck H., Zhu X.X. Building Instance Classification Using Street View Images. ISPRS Journal of Photogrammetry and Remote Sensing. 2018;145:44–59. https://doi.org/10.1016/j.isprsjprs.2018.02.006
14. Roussel R., Jacoby S., Asadipour A. Robust Building Identification from Street Views Using Deep Convolutional Neural Networks. Buildings. 2024;14(3). https://doi.org/10.3390/buildings14030578
15. Huang Yu., Zhuo L., Tao H., Shi Q., Liu K. A Novel Building Type Classification Scheme Based on Integrated LiDAR and High-Resolution Images. Remote Sensing. 2017;9(7). https://doi.org/10.3390/rs9070679
16. Ignatyev A.V., Gilka V.V., Matytsyna D.A. Automatic Recognition of Building Type for Environmental Monitoring System. Engineering Journal of Don. 2020;(1). (In Russ.). URL: http://www.ivdon.ru/en/magazine/archive/N1y2020/6266
17. Ryazanov S.S., Kulagina V.I. A Review of Russian and Foreign Sources of Multispectral Imagery for Agroecological Monitoring Systems Development. Russian Journal of Applied Ecology. 2024;(2):4–18. (In Russ.). https://doi.org/10.24852/2411-7374.2024.2.04.18
18. Bandam A., Busari E., Syranidou Ch., Linssen J., Stolten D. Classification of Building Types in Germany: A Data-Driven Modeling Approach. Data. 2022;7(4). https://doi.org/10.3390/data7040045
19. Lehner A., Blaschke Th. A Generic Classification Scheme for Urban Structure Types. Remote Sensing. 2019;11(2). https://doi.org/10.3390/rs11020173
20. Mikheeva T.I., Golovnin O.K., Elizarov V.V. Standard of Dislocation of Technical Equipment of Organization of Road Traffic and Geoobects of the Street-Road Network on the Electronic Map. In: Modern Problems of Life Safety: Intelligent Transport Systems and Situational Centers: Collection of Materials of V International Research and Practice Conference, 27–28 February 2018, Kazan, Russia. Kazan: Center for Innovation Technologies; 2018. P. 261–269. (In Russ.).
21. Smolev A.M., Mikheeva T.I., Zolotovitsky A.V. Methods of Modeling an Address Plan Within the Framework of Geographic Information System. In: IT & Transport: sbornik nauchnykh statei: Volume 24. Samara: IntelTranS; 2023. P. 4–13. (In Russ.).
22. Zuev V.N. Network Anomalies Detection by Deep Learning. Software & Systems. 2021;(1):91–97. (In Russ.). https://doi.org/10.15827/0236-235X.133.091-097
23. Lygin V.S., Sirota A.A., Golovinski P.A. Regularization of the Learning Process of Graph Neural Networks Using the Label Propagation Method. Proceedings of Voronezh State University. Series: Systems Analysis and Information Technologies. 2024;(3):92–101. (In Russ.). https://doi.org/10.17308/sait/1995-5499/2024/3/92-101
