Tsunami Building Damage Assessment using Multiclass Segmentation Model

Authors

Keywords:

Spatial Channel Attention, U-Net, Remote Sensing, Deep Learning, Semantic Segmentation

Abstract

Natural Disaster is an event caused by environment, it has been concerned as it can caused casualties that makes manual damage assessment become inefficient. Automated damage assessment is one of field of study in Remote Sensing which already studied for several years, from using Traditional Machine Learning into Deep Learning. Recently, semantic segmentation with multitemporal fusion is a method used for Damage Assessment using Deep Learning. Multitemporal Fusion is a method fusing two features from Pre and Post Disaster Images as one using concatenation to get the feature of all two images. Semantic Segmentation is a method to classify each pixel in images into specified class given. This research creates Baseline Model (ResNet-50 + Panoptic FPN + Multitemporal Fusion) for comparison with our proposed method, called SCAMU-Net, which consists of U-Net (with different backbone, DenseNet 121, 169, and 201 layers) and followed by Spatial Channel Attention Module (SCAM) using xBD Dataset in Sunda and Palu Dataset. According to finding of the study, SCAMU-Net with DenseNet 121 shows biggest result in Macro F1 in Palu Dataset with 89.8% outperforms the Baseline Model about 3.6%. Sunda Dataset cannot perform for Training and Testing caused by destroyed class too few for Models to generalized. SCAMU-Net has 1,203,549 less parameters than baseline model. SCAMU-Net also good for detecting different class (No Damaged and Destroyed) that adjacent each other. Results shown that SCAMU-Net DenseNet 121 is enough for classify damage in this research, it shown that extending from DenseNet 121 provide no significant results.

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Tsunami Sunda Strait and Palu 2018 Location

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Published

16.12.2022

How to Cite

Calvin Surya Widjaja, Alexander Agung Santoso Gunawan, & Edy Irwansyah. (2022). Tsunami Building Damage Assessment using Multiclass Segmentation Model. International Journal of Intelligent Systems and Applications in Engineering, 10(4), 498–506. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2314

Issue

Vol. 10 No. 4 (2022)

Section

Research Article

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