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All Journal Bulletin of Electrical Engineering and Informatics JOIV : International Journal on Informatics Visualization Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat
Arief, Ardiaty
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Public Health

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Journal : Bulletin of Electrical Engineering and Informatics

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Transformers for aerial images semantic segmentation of natural disaster-impacted areas in natural disaster assessment Wiria Nugraha, Deny; Ahmad Ilham, Amil; Achmad, Andani; Arief, Ardiaty
Bulletin of Electrical Engineering and Informatics Vol 14, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i2.8454

Abstract

Aerial image segmentation of natural disaster-impacted areas and detailed and automatic natural disaster assessment are the main focus of this study. Detecting and recognizing objects on aerial images of areas impacted by natural disasters and assessing natural disaster-impacted areas are still difficult problems. To solve these problems, this study utilizes four of the latest transformer-based semantic segmentation network models, bidirectional encoder representation from image transformers (BEIT), dense prediction transformer (DPT), OneFormer, and SegFormer, and proposes a detailed and automatic natural disaster assessment of the segmented image. The SegFormer model achieved the first-best result, and the OneFormer model achieved the second-best result. The SegFormer model outperformed OneFormer by 1.58% higher for the mean accuracy value and 4.28% for the mean intersection over union (mIoU) value. All receiver operating characteristics (ROC) curves have mean area under curve (AUC) values above 0.9, which means that the SegFormer model performs well in generating semantic segmentation images. The fuzzy c-means (FCM) clustering algorithm performed well and could automatically cluster the natural disaster assessments into four categories. This study has produced semantic segmentation of aerial images of areas impacted by natural disasters and natural disaster assessments, which can be used in natural disaster management systems.
Co-Authors ., Yusran Adil, Andi Eri Andika Ahmad Ilham, Amil Akil, Yusri Syam Amil Ahmad Ilham Andani Achmad Ansar Suyuti Arianti Said, Ida B, Ian Adrian Bachtiar Nappu, Muhammad Bachtiar Nappu Deny Wiria Nugraha Dewi, Dianti Utami Faizal Arya Samman Gassing . Gunadin, Indar Chaerah Hasniaty Ikhlas Kitta Indar Dewa, Yoga Ishak Salim, Ishak May Sweetha, Nabila Muhammad Ilham Musywirah Hamka, Icha Musywirah Hamka Nappu, Muhammad Bahtiar Nurlela, Andi Palantei, Elyas Palantei, Idris Salam, Andi Ejah Umraeni Salama Manjang Sri Mawar Said Subir, Ade Nur Fatimah