Water Bodies Extraction from Remote Sensing Data by Comparison of Deep Learning Models

Document Type : Research Article

Authors

1 MSc Student, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

2 Assistant Professor, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran

3 Assistant Professor, School of Engineering, Damghan University, Damghan, Iran.

Abstract

In the current century, increasing of greenhouse gases has led to significant changes in the climate. It is causing irreversible impacts on agricultural lands, food production, and drinking water supply. By using remote sensing technology and the processing of satellite data, aerial and drone imagery to collect information from the Earth surface, environmental changes monitoring and analyze water bodies has become an effective tool for planning and optimal management of water resources. Modern and interdisciplinary technologies have enabled water resource specialists to accurately identify, mapping and assess surface water resources. In this study, with the aim of identifying small water bodies using remote sensing data, four deep learning models -ENet, SegNet, SE U-Net, and DeepLabV3+EfficientNet- were trained over 50 epochs using the Binary Cross-Entropy loss function. The results showed that the DeepLabV3+EfficientNet model with a Precision of 96.09% and an IoU of 89.13%, achieved the best performance in detecting agricultural ponds. Additionally, the SegNet model with a Precision of 93.81%, and the DeepLabV3+EfficientNet model with an IoU of 85.58%, demonstrated the best performance in detecting of swimming pools. Based on these results, the DeepLabV3+EfficientNet model is recommended by this research for pools and reservoirs detection.

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Main Subjects

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Iranian journal of Ecohydrology
Volume 11, Issue 3
October 2024
Pages 321-336

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History

  • Receive Date: 08 July 2024
  • Revise Date: 20 August 2024
  • Accept Date: 21 September 2024
  • First Publish Date: 22 September 2024
  • Publish Date: 22 September 2024

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