DETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK
Surveying the region is an important step to do for a recovery stage after disaster. By surveying the region, further action to fix the damages can be planned better. In this research, a means to survey the region using satellite imagery and artificial neural network is proposed. The usage of sat...
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id-itb.:805822024-01-30T13:21:53ZDETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK Hikari Hidayatulloh, Fikri Indonesia Final Project image processing, earthquake, artificial neural network, INSTITUT TEKNOLOGI BANDUNG https://digilib.itb.ac.id/gdl/view/80582 Surveying the region is an important step to do for a recovery stage after disaster. By surveying the region, further action to fix the damages can be planned better. In this research, a means to survey the region using satellite imagery and artificial neural network is proposed. The usage of satellite imagery is common practice for damage assesment after a disaster occured. By adding an implementation of artificial neural network in the form of Unet-VGG16 model, damage classification for buildings can be performed. The model has shown a relatively low mistake indicated by loss function below 0.05. Further tests are carried using image data of the Cianjur earthquake in November 2022. A qualitative analysis shown that while losing precise details of the damage, the model has achievedthe purpose of giving accurate general assesment on the overall damage that occured in the region. text |
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Institut Teknologi Bandung |
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Indonesia Indonesia |
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Indonesia |
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Surveying the region is an important step to do for a recovery stage after disaster. By surveying
the region, further action to fix the damages can be planned better. In this research, a means to
survey the region using satellite imagery and artificial neural network is proposed. The usage of
satellite imagery is common practice for damage assesment after a disaster occured. By adding
an implementation of artificial neural network in the form of Unet-VGG16 model, damage
classification for buildings can be performed. The model has shown a relatively low mistake
indicated by loss function below 0.05. Further tests are carried using image data of the Cianjur
earthquake in November 2022. A qualitative analysis shown that while losing precise details of
the damage, the model has achievedthe purpose of giving accurate general assesment on the
overall damage that occured in the region.
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| format |
Final Project |
| author |
Hikari Hidayatulloh, Fikri |
| spellingShingle |
Hikari Hidayatulloh, Fikri DETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK |
| author_facet |
Hikari Hidayatulloh, Fikri |
| author_sort |
Hikari Hidayatulloh, Fikri |
| title |
DETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK |
| title_short |
DETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK |
| title_full |
DETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK |
| title_fullStr |
DETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK |
| title_full_unstemmed |
DETECTION FOR DAMAGE ON BUILDINGS AFTER EARTHQUAKE USING CONVOLUTIONAL NEURAL NETWORK |
| title_sort |
detection for damage on buildings after earthquake using convolutional neural network |
| url |
https://digilib.itb.ac.id/gdl/view/80582 |
| _version_ |
1822281660008235008 |