Coastline Segmentation on Landsat 8 OLI Images Using Majority Voting with Deep Learning Models

Nur Nafi'iyah

Abstract


Coastlines are highly dynamic due to both natural processes and anthropogenic factors such as global warming and sea level rise. Accurate coastline segmentation is essential for monitoring and management. Previous research applied deep learning for coastline detection, but individual models often produced suboptimal results, such as blurred boundaries or unwanted artifacts. This research proposes an automated coastline segmentation method using a majority voting strategy that combines three deep learning models: ResNet50, ResNet18, and MobileNet-V2. Landsat 8 OLI imagery is used for training and testing. The Jaccard index evaluation shows that ResNet18, ResNet50, and MobileNet-V2 achieved 0.96, 0.98, and 0.95 respectively, while the majority voting method also achieved 0.98. Although the ensemble score is numerically similar to the best single model (ResNet50), majority voting improves the consistency of segmentation by reducing artifacts such as additional circles outside land areas or cracks within land masses. These findings demonstrate that combining segmentation results provides more robust coastline detection compared to using individual models. Future work will apply this approach to Indonesian coastal data to further evaluate its generalizability across diverse shoreline conditions.

Keywords


deep learning, landsat 8 OLI, majority voting, segmentation automation, segmentation coastline

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References


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DOI: http://doi.org/10.11591/ijict.v15i2.pp588-596

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