Abstract Applications including change detection, disaster management, and urban planning require precise building information, and therefore automatic building extraction has become a significant research topic. With the improvements in sensor and satellite technologies, more data has become available, and with the increased computational power, deep learning methods have emerged as successful tools. In this study, U-Net and FPN architectures using four different backbones (ResNet-50, ResNeXt-50, SE-ResNext-50, and DenseNet-121), and an Attention Residual U-Net approach were used for building extraction from high-resolution aerial images. Two publicly available datasets, Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset were used to train and test the models. According to the results, Attention Residual U-Net model has the highest F1 score with 0.8154, IoU score with 0.7102, and test accuracy with 94.51% on the Inria dataset. On the Massachusetts dataset, FPN Dense-Net-121 model has the highest F1 score with 0.7565 and IoU score with 0.6188, and Attention Residual U-Net model has the highest test accuracy with 92.43%. It has been observed that, FPN with DenseNet backbone can be a better choice when working with small size datasets. On the other hand, Attention Residual U-Net approach achieved higher success when a sufficiently large dataset is provided.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images


    Beteiligte:

    Erschienen in:

    Advances in Space Research ; 71 , 7 ; 3076-3089


    Erscheinungsdatum :

    2022-05-06


    Format / Umfang :

    14 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Automatic Building Extraction from Aerial Images

    Gruen, A. / Nevatia, R. | British Library Online Contents | 1998


    Building Detection from Multiple Aerial Images in Dense Urban Areas

    Fradkin, M. / Maitre, H. / Roux, M. | British Library Online Contents | 2001


    Automatic extraction of road network from aerial images

    Xuemei Ding, / Wenjing Kang, / Jiwen Cui, et al. | IEEE | 2006


    Fusion of high resolution lidar and aerial images for object extraction

    Mumtaz, Salman Ali / Mooney, Kevin | IEEE | 2008


    Accurate Building Structure Recovery from High Resolution Aerial Imagery

    Cord, M. / Jordan, M. / Cocquerez, J.-P. | British Library Online Contents | 2001