Building Damage Assessment Using Feature Concatenated Siamese Neural Network
Fast and accurate post-earthquake building damage assessment is an important task to do to define search and rescue procedures. Many approaches have been proposed to automate this process by using artificial intelligence, some of which use handcrafted features that are considered inefficient. This r...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10418211/ |
| _version_ | 1797319544123949056 |
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| author | Mgs M. Luthfi Ramadhan Grafika Jati Wisnu Jatmiko |
| author_facet | Mgs M. Luthfi Ramadhan Grafika Jati Wisnu Jatmiko |
| author_sort | Mgs M. Luthfi Ramadhan |
| collection | DOAJ |
| description | Fast and accurate post-earthquake building damage assessment is an important task to do to define search and rescue procedures. Many approaches have been proposed to automate this process by using artificial intelligence, some of which use handcrafted features that are considered inefficient. This research proposed end-to-end building damage assessment based on a Siamese neural network. We modify the network by adding a feature concatenation mechanism to enrich the data feature. This concatenation mechanism creates different features based on each output from the convolution block. It concatenates them into a high-dimensional vector so that the feature representation is more likely to be linearly separable, resulting in better discrimination capability than the standard siamese. Our model was evaluated through three experimental scenarios where we performed classification of G1 or G5, G1-G4 or G5, and all the five grades of EMS-98 building damage description. Our models are superior to the standard Siamese neural network and state-of-the-art in this field. Our model obtains f1-scores of 79.47%, 54.09%, 40.64% and accuracy scores of 87.24%, 95.28%, and 42.57% for the first, second, and third experiments, respectively. |
| first_indexed | 2024-03-08T04:08:26Z |
| format | Article |
| id | doaj.art-4d5e375ddc964a6cb2eb0ed36cabb59d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T04:08:26Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4d5e375ddc964a6cb2eb0ed36cabb59d2024-02-09T00:03:34ZengIEEEIEEE Access2169-35362024-01-0112191001911610.1109/ACCESS.2024.336128710418211Building Damage Assessment Using Feature Concatenated Siamese Neural NetworkMgs M. Luthfi Ramadhan0https://orcid.org/0000-0001-8571-8924Grafika Jati1Wisnu Jatmiko2https://orcid.org/0000-0002-0530-7955Faculty of Computer Science, University of Indonesia, Depok, IndonesiaFaculty of Computer Science, University of Indonesia, Depok, IndonesiaFaculty of Computer Science, University of Indonesia, Depok, IndonesiaFast and accurate post-earthquake building damage assessment is an important task to do to define search and rescue procedures. Many approaches have been proposed to automate this process by using artificial intelligence, some of which use handcrafted features that are considered inefficient. This research proposed end-to-end building damage assessment based on a Siamese neural network. We modify the network by adding a feature concatenation mechanism to enrich the data feature. This concatenation mechanism creates different features based on each output from the convolution block. It concatenates them into a high-dimensional vector so that the feature representation is more likely to be linearly separable, resulting in better discrimination capability than the standard siamese. Our model was evaluated through three experimental scenarios where we performed classification of G1 or G5, G1-G4 or G5, and all the five grades of EMS-98 building damage description. Our models are superior to the standard Siamese neural network and state-of-the-art in this field. Our model obtains f1-scores of 79.47%, 54.09%, 40.64% and accuracy scores of 87.24%, 95.28%, and 42.57% for the first, second, and third experiments, respectively.https://ieeexplore.ieee.org/document/10418211/Classificationdeep learningdisasterearthquakeLiDARsiamese neural network |
| spellingShingle | Mgs M. Luthfi Ramadhan Grafika Jati Wisnu Jatmiko Building Damage Assessment Using Feature Concatenated Siamese Neural Network IEEE Access Classification deep learning disaster earthquake LiDAR siamese neural network |
| title | Building Damage Assessment Using Feature Concatenated Siamese Neural Network |
| title_full | Building Damage Assessment Using Feature Concatenated Siamese Neural Network |
| title_fullStr | Building Damage Assessment Using Feature Concatenated Siamese Neural Network |
| title_full_unstemmed | Building Damage Assessment Using Feature Concatenated Siamese Neural Network |
| title_short | Building Damage Assessment Using Feature Concatenated Siamese Neural Network |
| title_sort | building damage assessment using feature concatenated siamese neural network |
| topic | Classification deep learning disaster earthquake LiDAR siamese neural network |
| url | https://ieeexplore.ieee.org/document/10418211/ |
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