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• W3204877369 abstract "Building rooftop data are of importance in several urban applications and in natural disaster management. In contrast to traditional surveying and mapping, by using high spatial resolution aerial images, deep learning-based building rooftops extraction methods are efficient and accurate. Although more training data is preferred in deep learning-based tasks, the effect of data volume on building extraction models is underexplored. Therefore, the paper explores the impact of data volume on the performance of building rooftop extraction from very-high-spatial-resolution (VHSR) images using deep learning-based methods. To do so, we manually labelled 0.12m spatial resolution aerial images and perform a comparative analysis of models trained on datasets of different sizes using popular deep learning architectures for segmentation tasks, including Fully Convolutional Networks (FCN)-8s, U-Net and DeepLabv3+. The experiments showed that with more training data, algorithms converged faster and achieved higher accuracy, while better algorithms were able to better mitigate the lack of training data." @default.
• W3204877369 created "2021-10-11" @default.
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• W3204877369 date "2021-03-16" @default.
• W3204877369 modified "2023-10-16" @default.
• W3204877369 title "The impact of data volume on performance of deep learning based building rooftop extraction using very high spatial resolution aerial images" @default.
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• W3204877369 doi "https://doi.org/10.48550/arxiv.2103.09300" @default.
• W3204877369 hasPublicationYear "2021" @default.
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