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U-Nets and Multispectral Images for Detecting the Surface Water of Rivers via SAR Images

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Geographical Information Systems Theory, Applications and Management (GISTAM 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2107))

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Abstract

Global water resources are under increasing pressure due to demands from population growth and climate change. As a result, the regime of the rivers is changing and their ecosystems are threatened. Therefore, for effective water management and mitigation of hazards, it is crucial to frequently and accurately map the surface area of river water. Synthetic Aperture Radar (SAR) backscatter images at high temporal resolution are nowadays available. However, mapping the surface water of narrow water bodies, such as rivers, remains challenging due to the SAR spatial resolution (few tens of meters). Conversely, Multi-Spectral Instrument (MSI) images have a higher spatial resolution (few meters) but are affected by cloud coverage. In this paper, we present a new method for automatic detection and mapping of the surface water of rivers. The method is based on the convolutional neural network known as U-Net. To develop the proposed approach, two datasets are needed: (i) a set of Sentinel-2 MSI images, used to achieve target values; (ii) a set of Sentinel-1A SAR backscatter images, used as input values. The proposed method has been experimented to map the surface water of the Mijares river (Spain) from April 2019 to September 2022. Experimental results show that the proposed approach computes the total surface area covered by the river water with a mean absolute error equal to 0.072, which is very promising for the target application. To encourage scientific collaborations, the source code used for this work has been made publicly available.

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Acknowledgements

This work has been partially supported by: (i) the National Center for Sustainable Mobility MOST/Spoke10, funded by the Italian Ministry of University and Research, in the framework of the National Recovery and Resilience Plan; (ii) the PRA_2022_101 project “Decision Support Systems for territorial networks for managing ecosystem services”, funded by the University of Pisa; (iii) the Ministry of University and Research (MUR) as part of the PON 2014–2020 “Research and Innovation" resources – “Green/Innovation Action – DM MUR 1061/2022”; (iv) the Italian Ministry of University and Research (MUR), in the framework of the "Reasoning" project, PRIN 2020 LS Programme, Project number 2493 04–11–2021; (v) the Italian Ministry of Education and Research (MIUR) in the framework of the FoReLab project (Departments of Excellence).

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Authors and Affiliations

  1. Department of Information Engineering, University of Pisa, Via G. Caruso 16, 56122, Pisa, Italy

    Diana Orlandi, Federico A. Galatolo, Mario G. C. A. Cimino, Pierfrancesco Foglia & Cosimo A. Prete

  2. Department of Earth Sciences, University of Pisa, Via Santa Maria 53, 56126, Pisa, Italy

    Alessandro La Rosa & Carolina Pagli

Authors
  1. Diana Orlandi
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  2. Federico A. Galatolo
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  3. Alessandro La Rosa
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  4. Mario G. C. A. Cimino
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  5. Pierfrancesco Foglia
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  6. Carolina Pagli
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  7. Cosimo A. Prete
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Correspondence to Mario G. C. A. Cimino .

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Editors and Affiliations

  1. Polytechnic Institute of Setúbal/IPS, Setúbal, Portugal

    Cédric Grueau

  2. NOVA School of Science and Technology, Caparica, Portugal

    Armanda Rodrigues

  3. Hellenic Open University, Patra, Greece

    Lemonia Ragia

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Orlandi, D. et al. (2024). U-Nets and Multispectral Images for Detecting the Surface Water of Rivers via SAR Images. In: Grueau, C., Rodrigues, A., Ragia, L. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2023. Communications in Computer and Information Science, vol 2107. Springer, Cham. https://doi.org/10.1007/978-3-031-60277-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-60277-1_1

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