Masking preprocessing in transfer learning for damage building detection

Toba H.; Bunyamin H.; Widyaya J.E.Wibisono C.Haryadi L.S.

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Masking preprocessing in transfer learning for damage building detection

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The sudden climate change occurring in different places in the world has made disasters more unpredictable than before. In addition, responses are often late due to manual processes that have to be performed by experts. Consequently, major advances in computer vision (CV) have prompted researchers to develop smart models to help these experts. We need a strong image representation model, but at the same time, we also need to prepare for a deep learning environment at a low cost. This research attempts to develop transfer learning models using low-cost masking pre-processing in the experimental building damage (xBD) dataset, a large-scale dataset for advancing building damage assessment. The dataset includes eight types of disasters located in fifteen different countries and spans thousands of square kilometers of satellite images. The models are based on U-Net, i.e., AlexNet, visual geometry group (VGG)-16, and ResNet-34. Our experiments show that ResNet-34 is the best with an F1 score of 71.93, and an intersection over union (IoU) of 66.72. The models are built on a resolution of 1,024 pixels and use only first-tier images compared to the state-of-the-art baseline. For future orientations, we believe that the approach we propose could be beneficial to improve the efficiency of deep learning training. © 2023, Institute of Advanced Engineering and Science. All rights reserved.

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《IAES International Journal of Artificial Intelligence》 |2023年第2期|552-559|共8页
作者
Toba H.; Bunyamin H.; Widyaya J.E.Wibisono C.Haryadi L.S.;
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作者单位

Master Program in Computer Science, Faculty of Information Technology, Maranatha Christian University;

Bachelor Program in Informatics Engineering, Faculty of Information Technology, Maranatha Christian University;

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正文语种英语
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Classification; Convolutional neural network; Damage building detection; Image segmentation; Transfer learning;

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Masking preprocessing in transfer learning for damage building detection

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