Improving Semantic Segmentation of Flood Areas Using Rotation and Flipping-Based Feature Augmentation

Naili Suri  Intizhami; Eka Qadri  Nuranti; Nur Inaya  Bahar

doi:10.52436/1.jutif.2025.6.3.4564

Authors

Naili Suri Intizhami Computer Science, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
Eka Qadri Nuranti Computer Science, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia
Nur Inaya Bahar Computer Science, Institut Teknologi Bacharuddin Jusuf Habibie, Indonesia

DOI:

https://doi.org/10.52436/1.jutif.2025.6.3.4564

Keywords:

Deep Learning, Feature Augmentation, Fully Convolutional Network (FCN), Semantic Segmentation

Abstract

Semantic segmentation is one of the powerful methods for analyzing flood video or picture data captured by smartphones. However, achieving accurate semantic segmentation requires the application of several methods. In this work, we address the task of feature augmentation approach using rotation (90°, 180°, 270°) and flipping (horizontal, vertical) to improve semantic segmentation of flood areas in Parepare city using a Fully Convolutional Network (FCN). The experimental results demonstrate that the best augmentation scenario 270° rotation achieved an accuracy of 88% and 90° rotation achieved an mean Intersection over Union (mIoU) of 43%, significantly outperforming the baseline FCN model without augmentation, which achieved 86% accuracy and 35% mIoU.

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