RivUNet: An Attention-Gated Deep U-Net for Water Body Segmentation in Satellite Imagery to Enhance River Encroachment Monitoring

Mahin Montasir Afif (), Abdullah Al Noman (), K. M. Tahsin Kabir (), Md. Mortuza Ahmmed (), Md. Ashaful Babu () and Mohamed Lahby ()
Additional contact information
Mahin Montasir Afif: American International University-Bangladesh
Abdullah Al Noman: American International University-Bangladesh
K. M. Tahsin Kabir: Asian University of Bangladesh
Md. Mortuza Ahmmed: American International University-Bangladesh
Md. Ashaful Babu: Independent University, Bangladesh
Mohamed Lahby: ENS, University Hassan 2 of Casablanca

A chapter in Generative AI and Optimization Techniques for Sustainable Water Management, 2026, pp 135-153 from Springer

Abstract: Abstract Effective water body mapping is fundamental to sustainable water resource management and river conservation in urban and semi-urban regions which face the risk of encroachment. In this chapter, we propose a customized Attention U-Net architecture called RivUNet that is specifically tailored for the semantic segmentation of water bodies in satellite imagery. The model is trained on the Satellite Images of Water Bodies dataset, comprising Sentinel-2 imagery accompanied by binary masks generated through the Normalized Difference Water Index (NDWI). TThese masks distinguish water from non-water regions, which enables a focused learning process for water body segmentation. The proposed model enhances the traditional U-Net architectures by incorporating attention gates within the skip connections, which allow the model to selectively emphasize spatially important features during the decoding process. The proposed model achieves a testing accuracy of 91% and an AUC score of 0.95, demonstrating its capability to generalize across diverse scenes and water body structures. This segmentation technique can be adopted to monitor how much river encroachment has occurred if analyzed, which accelerates river encroachment monitoring. This chapter lays a strong foundation for using semantic segmentation techniques in water management applications, including temporal river monitoring, hydrological analysis, and urban planning support.

Keywords: Attention U-Net; Segmentation; River Encroachment; Water Resource Management (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-032-19012-3_9

Ordering information: This item can be ordered from
http://www.springer.com/9783032190123

DOI: 10.1007/978-3-032-19012-3_9

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().