Geomatics and Environmental Engineering, vol. 19, no. 4

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Vol. 19 No. 4 (2025): Geomatics and Environmental Engineering

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Submitted
Dec 5, 2024
Accepted
Jun 16, 2025
Published
Jul 26, 2025
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This work is licensed under a Creative Commons Attribution 4.0 International License.

River Area Segmentation Using Sentinel-1 SAR Imagery with Deep-Learning Approach

https://doi.org/10.7494/geom.2025.19.4.39
Ni Putu Karisma Dewi
Ganesha University of Education, Faculty of Engineering and Vocational, Computer Science, Singaraja, Indonesia
Putu Hendra Suputra
Ganesha University of Education, Faculty of Engineering and Vocational, Software Engineering Technology, Singaraja, Indonesia
https://orcid.org/0000-0003-1521-9307
A.A. Gede Yudhi Paramartha
Ganesha University of Education, Faculty of Engineering and Vocational, Computer Science, Singaraja, Indonesia
https://orcid.org/0000-0003-2640-3433
Luh Joni Erawati Dewi
Ganesha University of Education, Faculty of Engineering and Vocational, Software Engineering Technology, Singaraja, Indonesia
https://orcid.org/0000-0003-3041-731X
Pariwate Varnakovida
King Mongkut’s University of Technology Thonburi, Faculty of Science, Department of Mathematics, Bangkok, Thailand
https://orcid.org/0000-0003-3425-3447
Kadek Yota Ernanda Aryanto
Ganesha University of Education, Faculty of Engineering and Vocational, Computer Science, Singaraja, Indonesia
https://orcid.org/0000-0002-0368-890X

Corresponding Author(s) : Kadek Yota Ernanda Aryanto

yota.ernanda@undiksha.ac.id

Geomatics and Environmental Engineering, Vol. 19 No. 4 (2025): Geomatics and Environmental Engineering

Abstract

River segmentation is important in delivering essential information for environmental analytics such as water management, flood/disaster management, observations of climate change, or human activities. Advances in remote-sensing technology have provided more complex features that limit the traditional approaches’ effectiveness. This work uses deep-learning-based models to enhance river extractions from satellite imagery. With Resnet-50 as the backbone network, CNN U-Net and DeepLabv3+ were utilized to perform the river segmentation of the Sentinel-1 C-Band synthetic aperture radar (SAR) imagery. The SAR data was selected due to its capability to capture surface details regardless of weather conditions, with VV+VH band polarizations being employed to improve water surface reflectivity. A total of 1080 images were utilized to train and test the models. The models’ performance was measured using the Dice coefficient. The CNN U-Net architecture achieved an accuracy of 0.94, while DeepLabv3+ attained an accuracy of 0.92. Although DeepLabv3+ showed more stability during the training and performed better on wider rivers, CNN U-Net excelled at identifying narrow rivers. In conclusion, a river-segmentation model was conducted using Sentinel-1 C-Band SAR data, with CNN U-Net outperforming DeepLabv3+; this enabled detailed river mapping for irrigationand flood-monitoring applications – particularly in cloud-prone tropical regions.

Keywords

river segmentation satellite imagery remote sensing deep learning CNN U-Net DeepLabv3
Dewi, N. P. K., Suputra, P. H. ., Paramartha, A. G. Y. ., Dewi, L. J. E. ., Varnakovida, P. ., & Aryanto, K. Y. E. (2025). River Area Segmentation Using Sentinel-1 SAR Imagery with Deep-Learning Approach. Geomatics and Environmental Engineering, 19(4), 39–63. https://doi.org/10.7494/geom.2025.19.4.39
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