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Deep Learning Residential Building Segmentation for Evaluation of Suburban Areas Development

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Computational Science – ICCS 2024 (ICCS 2024)

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Abstract

Deep neural network models are commonly used in computer vision problems, e.g., image segmentation. Convolutional neural networks have been state-of-the-art methods in image processing, but new architectures, such as Transformer-based approaches, have started outperforming previous techniques in many applications. However, those techniques are still not commonly used in urban analyses, mostly performed manually. This paper presents a framework for the residential building semantic segmentation architecture as a tool for automatic urban phenomena monitoring. The method could improve urban decision-making processes with automatic city analysis, which is predisposed to be faster and even more accurate than those made by human researchers. The study compares the application of new deep network architectures with state-of-the-art solutions. The analysed problem is urban functional zone segmentation for the urban sprawl evaluation using targeted land cover map construction. The proposed method monitors the expansion of the city, which, uncontrolled, can cause adverse effects. The method was tested on photos from three residential districts. The first district has been manually segmented by functional zones and used for model training and evaluation. The other two districts have been used for automated segmentation by models’ inference to test the robustness of the methodology. The test resulted in 98.2% accuracy.

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Authors and Affiliations

  1. Institute of Physics, Jan Kochanowski University, ul. Uniwersytecka 7, 25-406, Kielce, Poland

    Agnieszka Łysak

  2. Faculty of Mathematics and Information Science, Warsaw University of Technology, ul. Koszykowa 75, 00-662, Warsaw, Poland

    Marcin Luckner

Authors
  1. Agnieszka Łysak
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Correspondence to Agnieszka Łysak .

Editor information

Editors and Affiliations

  1. University of Malaga, Malaga, Spain

    Leonardo Franco

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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Łysak, A., Luckner, M. (2024). Deep Learning Residential Building Segmentation for Evaluation of Suburban Areas Development. In: Franco, L., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2024. ICCS 2024. Lecture Notes in Computer Science, vol 14838. Springer, Cham. https://doi.org/10.1007/978-3-031-63783-4_9

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  • DOI: https://doi.org/10.1007/978-3-031-63783-4_9

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