Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks
Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are nee...
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| Format: | Article |
| Language: | English |
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LPPM STIKI Malang
2025-07-01
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| Series: | J-Intech (Journal of Information and Technology) |
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| Online Access: | https://jurnal.stiki.ac.id/J-INTECH/article/view/1919 |
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| author | Revaldi Rahmatmulya Agung Teguh Wibowo Almais Mokhamad Amin Hariyadi |
| author_facet | Revaldi Rahmatmulya Agung Teguh Wibowo Almais Mokhamad Amin Hariyadi |
| author_sort | Revaldi Rahmatmulya |
| collection | DOAJ |
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Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. The data utilized consists of aerial images sourced from xView2: Assess Building Damage, comprising 50 aerial images with 5 classes: no-damage, minor-damage, major-damage, destroyed, and unlabeled. The steps undertaken in this research include data preprocessing using patchify and data augmentation. Subsequently, feature extraction is performed using convolution, followed by the training process using a neural network with the proposed architecture. This study proposes an architecture with 27 hidden layers, with feature extraction utilizing average pooling. The model evaluation process will employ Mean Intersection over Union (MIoU) to assess how closely the segmentation prediction results resemble the original data. The proposed architecture demonstrates the best MIoU result with a value of 0.31 and an accuracy of 0.9577.
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| format | Article |
| id | doaj-art-1ce28fa3162246d68f66b44761476094 |
| institution | Kabale University |
| issn | 2303-1425 2580-720X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | LPPM STIKI Malang |
| record_format | Article |
| series | J-Intech (Journal of Information and Technology) |
| spelling | doaj-art-1ce28fa3162246d68f66b447614760942025-08-20T03:29:35ZengLPPM STIKI MalangJ-Intech (Journal of Information and Technology)2303-14252580-720X2025-07-01130110.32664/j-intech.v13i01.1919Post-Disaster Building Damage Segmentation Using Convolutional Neural NetworksRevaldi Rahmatmulya0Agung Teguh Wibowo Almais1Mokhamad Amin Hariyadi2Universitas Islam Negeri Maulana Malik Ibrahim Malang, IndonesiaUniversitas Islam Negeri Maulana Malik Ibrahim Malang, IndonesiaUniversitas Islam Negeri Maulana Malik Ibrahim Malang, Indonesia Natural disasters are events caused by nature such as earthquakes, tornadoes, tsunamis, forest fires, and others. The impacts of natural disasters are significant and varied across various sectors, including the economy, health, and primarily, infrastructure. Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. The data utilized consists of aerial images sourced from xView2: Assess Building Damage, comprising 50 aerial images with 5 classes: no-damage, minor-damage, major-damage, destroyed, and unlabeled. The steps undertaken in this research include data preprocessing using patchify and data augmentation. Subsequently, feature extraction is performed using convolution, followed by the training process using a neural network with the proposed architecture. This study proposes an architecture with 27 hidden layers, with feature extraction utilizing average pooling. The model evaluation process will employ Mean Intersection over Union (MIoU) to assess how closely the segmentation prediction results resemble the original data. The proposed architecture demonstrates the best MIoU result with a value of 0.31 and an accuracy of 0.9577. https://jurnal.stiki.ac.id/J-INTECH/article/view/1919convolutional neural networkmachine learningsegmentasi |
| spellingShingle | Revaldi Rahmatmulya Agung Teguh Wibowo Almais Mokhamad Amin Hariyadi Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks J-Intech (Journal of Information and Technology) convolutional neural network machine learning segmentasi |
| title | Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks |
| title_full | Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks |
| title_fullStr | Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks |
| title_full_unstemmed | Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks |
| title_short | Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks |
| title_sort | post disaster building damage segmentation using convolutional neural networks |
| topic | convolutional neural network machine learning segmentasi |
| url | https://jurnal.stiki.ac.id/J-INTECH/article/view/1919 |
| work_keys_str_mv | AT revaldirahmatmulya postdisasterbuildingdamagesegmentationusingconvolutionalneuralnetworks AT agungteguhwibowoalmais postdisasterbuildingdamagesegmentationusingconvolutionalneuralnetworks AT mokhamadaminhariyadi postdisasterbuildingdamagesegmentationusingconvolutionalneuralnetworks |