Udomchaiporn, Akadej

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Udomchaiporn, Akadej
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akadej.ud@kmitl.ac.th

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    Item type:Publication,
    Comparative Analysis of Deep Learning Models for Building Extraction from High-resolution Satellite Imagery
    (2025-01-01)
    Chueprasert, Tachasit
    ;
    Udomchaiporn, Akadej
    ;
    Intagosum, Sarun
    In this research, an approach to extract buildings from Google's satellite imagery was proposed. The performances of various deep learning models (U-Net, RIU-Net, U-Net++, Res-U-Net, and DeepLabV3+) on pre-processed datasets were compared. The models were trained using the similarity metrics of Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). The best-performing models among the segmentation techniques were Res-U-Net and DeepLabV3+. Res-U-Net, an enhanced version of the traditional U-Net model that incorporates residual connections for improved feature propagation, achieved an F1 score of 85.43% when using the RGB dataset. Similarly, DeepLabV3+ also achieved high performance on the Enhanced RGB dataset, obtaining an F1 score of 85.18% after applying pre-processing techniques. This research highlights the significance of color as a dominant feature for accurate building extraction from satellite images. The findings contribute to improved methodologies for building identification, benefiting urban planning, and disaster management applications.