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Applicability of the Geospatial Segment Anything Model for Reservoir Extraction Using KOMPSAT-3/3A Satellite Imagery

  • Namhoon Kim
  • , Suhong Yoo
  • , Hanna Lee
  • , Sumin Park
  • , Gihong Kim*
  • *Corresponding author for this work
  • Korea Military Academy
  • Namseoul University
  • Gangneung-Wonju National University
  • Korea Aerospace Research Institute

Research output: Contribution to journalJournal articlepeer-review

Abstract

Accurate reservoir area data are essential for effective water resource management, yet traditional field surveys often face labor and logistical challenges. In this study, we evaluated the Geospatial Segment Anything Model (GeoSAM) in conjunction with high-resolution KOMPSAT-3/3A satellite imagery for reservoir delineation in the Korean Peninsula. Our experiments demonstrate that GeoSAM consistently achieves high accuracies (85.95–97.10%), surpassing the conventional normalized difference water index-based extraction method, which averaged 93.74%. Moreover, GeoSAM maintains robust performance under challenging conditions—such as frozen reservoirs, shadowed areas, and cloudy environments—by incorporating additional point prompts. These findings underscore the potential of GeoSAM to advance remote sensing applications in water resource management, particularly for small- and medium-sized urban areas.

Original languageEnglish
Pages (from-to)2589-2605
Number of pages17
JournalSensors and Materials
Volume37
Issue number4-6
DOIs
StatePublished - 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 6 - Clean Water and Sanitation
    SDG 6 Clean Water and Sanitation
  2. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • deep learning
  • image segmentation
  • KOMPSAT-3/3A
  • remote sensing
  • reservoir monitoring
  • Segment Anything Model
  • water body extraction

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