{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:19:43Z","timestamp":1772770783743,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T00:00:00Z","timestamp":1708300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The persistent increase in the magnitude of urban data, combined with the broad range of sensors from which it derives in modern urban environments, poses issues including data integration, visualization, and optimal utilization. The successful selection of suitable locations for predetermined commercial activities and public utility services or the reuse of existing infrastructure arise as urban planning challenges to be addressed with the aid of the aforementioned data. In our previous work, we have integrated a multitude of publicly available real-world urban data in a visual semantic decision support environment, encompassing map-based data visualization with a visual query interface, while employing and comparing several classifiers for the selection of appropriate locations for establishing parking facilities. In the current work, we challenge the best representative of the previous approach, i.e., random forests, with convolutional neural networks (CNNs) in combination with a graph-based representation of the urban input data, relying on the same dataset to ensure comparability of the results. This approach has been inspired by the inherent visual nature of urban data and the increased capability of CNNs to classify image-based data. The experimental results reveal an improvement in several performance indices, implying a promising potential for this specific combination in decision support for urban planning problems.<\/jats:p>","DOI":"10.3390\/s24041335","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T04:39:36Z","timestamp":1708317576000},"page":"1335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Enhancing Urban Data Analysis: Leveraging Graph-Based Convolutional Neural Networks for a Visual Semantic Decision Support System"],"prefix":"10.3390","volume":"24","author":[{"given":"Nikolaos","family":"Sideris","sequence":"first","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece"}]},{"given":"Georgios","family":"Bardis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0632-9769","authenticated-orcid":false,"given":"Athanasios","family":"Voulodimos","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Ingineering, National Technical University of Athens, 15780 Athens, Greece"}]},{"given":"Georgios","family":"Miaoulis","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer Engineering, University of West Attica, 12243 Athens, Greece"}]},{"given":"Djamchid","family":"Ghazanfarpour","sequence":"additional","affiliation":[{"name":"Faculty of Science and Technology, University of Limoges, CEDEX, 87060 Limoges, France"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,19]]},"reference":[{"key":"ref_1","unstructured":"Ritchie, H., and Roser, M. (2024, February 06). Urbanization. Our World Data. Available online: https:\/\/ourworldindata.org\/urbanization."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/s41893-022-00979-5","article-title":"A new urban narrative for sustainable development","volume":"6","author":"Keith","year":"2022","journal-title":"Nat. Sustain."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1016\/j.asej.2020.04.020","article-title":"Urban design & urban planning: A critical analysis to the theoretical relationship gap","volume":"12","author":"Asaad","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s42949-022-00048-y","article-title":"Urban land expansion: The role of population and economic growth for 300+ cities","volume":"2","author":"Mahtta","year":"2022","journal-title":"NPJ Urban Sustain."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"T\u00e9kouabou, S.C.K., Chenal, J., Azmi, R., Toulni, H., Diop, E.B., and Nikiforova, A. (2022). Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development. Data, 7.","DOI":"10.3390\/data7120170"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1149402","DOI":"10.3389\/fdata.2023.1149402","article-title":"Big data analytics and smart cities: Applications, challenges, and opportunities","volume":"6","author":"Cesario","year":"2023","journal-title":"Front. Big Data"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shi, W., Goodchild, M.F., Batty, M., Kwan, M.-P., and Zhang, A. (2021). Urban Informatics, Springer.","DOI":"10.1007\/978-981-15-8983-6"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.buildenv.2018.03.041","article-title":"Spatiotemporal impact of land use\/land cover changes on urban heat islands: A case study of Pa\u00e7o do Lumiar, Brazil","volume":"136","author":"Silva","year":"2018","journal-title":"Build. Environ."},{"key":"ref_9","unstructured":"Hamilton, W., Ying, Z., and Leskovec, J. (2017). Advances in Neural Information Processing Systems, Curran Associates, Inc.. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/hash\/5dd9db5e033da9c6fb5ba83c7a7ebea9-Abstract.html."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/s11036-013-0489-0","article-title":"Big Data: A Survey","volume":"19","author":"Chen","year":"2014","journal-title":"Mob. Netw. Appl."},{"key":"ref_11","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012). Advances in Neural Information Processing Systems, Curran Associates, Inc.. Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2012\/hash\/c399862d3b9d6b76c8436e924a68c45b-Abstract.html."},{"key":"ref_12","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., and Bengio, Y. (2018). Graph Attention Networks. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Krichen, M. (2023). Convolutional Neural Networks: A Survey. Computers, 12.","DOI":"10.3390\/computers12080151"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2018.04.050","article-title":"Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery","volume":"214","author":"Huang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, J., and Kwon, Y. (2023). Novel CNN-Based Approach for Reading Urban Form Data in 2D Images: An Application for Predicting Restaurant Location in Seoul, Korea. ISPRS Int. J. Geo-Inf., 12.","DOI":"10.3390\/ijgi12090373"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1080\/15481603.2021.1933367","article-title":"Automatic mapping of urban green spaces using a geospatial neural network","volume":"58","author":"Chen","year":"2021","journal-title":"GIScience Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bao, H., Ming, D., Guo, Y., Zhang, K., Zhou, K., and Du, S. (2020). DFCNN-Based Semantic Recognition of Urban Functional Zones by Integrating Remote Sensing Data and POI Data. Remote Sens., 12.","DOI":"10.3390\/rs12071088"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Sideris, N., Bardis, G., Voulodimos, A., Miaoulis, G., and Ghazanfarpour, D. (2019). Using Random Forests on Real-World City Data for Urban Planning in a Visual Semantic Decision Support System. Sensors, 19.","DOI":"10.3390\/s19102266"},{"key":"ref_19","first-page":"420","article-title":"Transfer learning using VGG-16 with Deep Convolutional Neural Network for Classifying Images","volume":"9","author":"Tammina","year":"2019","journal-title":"Int. J. Sci. Res. Publ."},{"key":"ref_20","unstructured":"Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Van Esesn, B.C., Awwal, A.A.S., and Asari, V.K. (2018). The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches. arXiv."},{"key":"ref_21","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. arXiv."},{"key":"ref_22","unstructured":"(2024, February 04). Research\u2014DataGrandLyon. Available online: https:\/\/data.grandlyon.com\/portail\/en\/recherche."},{"key":"ref_23","unstructured":"Kipf, T.N., and Welling, M. (2017). Semi-Supervised Classification with Graph Convolutional Networks. arXiv."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.physrep.2020.05.004","article-title":"Networks beyond pairwise interactions: Structure and dynamics","volume":"874","author":"Battiston","year":"2020","journal-title":"Phys. Rep."},{"key":"ref_25","unstructured":"Buttenfield, B.P., and McMaster, R.B. (2023, November 12). Map Generalization: Making Rules for Knowledge Representation. Citeseer. Available online: https:\/\/citeseerx.ist.psu.edu\/document?repid=rep1&type=pdf&doi=97fdf14eadea9ff72c058d4e4c4b24b6c58b346a."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1335\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:02:09Z","timestamp":1760104929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/4\/1335"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2,19]]},"references-count":25,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,2]]}},"alternative-id":["s24041335"],"URL":"https:\/\/doi.org\/10.3390\/s24041335","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,2,19]]}}}