Identification of Irrigation Canals using Deep Learning Techniques from Remote Sensing Data

Subject Areas : Journal of Radar and Optical Remote Sensing and GIS

Sina Khoshnevisan ¹ , Saeid Gharechelou ² , Erfan Khoshnevisan ³ , Samad Emamgholizadeh ⁴ , Fatemeh Khakzad ⁵ , Reza Vosoughmand ⁶

1 -
2 - Assistant Professor, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
3 - Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran
4 - 4. Professor, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
5 - 5. MSc. Student, Faculty of Civil Engineering, Shahrood University of Technology, Shahrood, Iran
6 - 6. BSc Student, Computer Department, Damghan Branch, Islamic Azad University, Damghan, Iran

Received: 2024-12-22 Accepted : 2025-05-20 Published : 2025-06-23

Keywords: Convolutional neural network, Deep learning, Earthen canal, Irrigation network, Satellite data,

Abstract :

The global water crisis has emerged as one of the most pressing challenges of recent decades, particularly in arid regions like Iran, where agriculture accounts for over 85% of water consumption. Efficient water management and conservation are critical to addressing this issue. Irrigation canals are essential for water allocation and reducing the water leakage in agricultural fields require precise and updated mapping to optimize their functionality. While concrete canals typically maintain stable maps, earthen canals experience continuous changes in position and dimensions, necessitating frequent monitoring and updates. Traditional manual mapping methods are time-consuming, costly, and inadequate for large-scale continuous monitoring. To overcome these limitations, this research assembled a dataset of high-resolution satellite images from Khuzestan province, Iran, with detailed labeling of irrigation canals. Semantic image segmentation techniques based on deep learning were then evaluated to identify and map these canals. The result showed among the six models tested in this research, the UNet3+ model achieved the highest performance with a recall of 86.64%, F1-score of 89.47%, and IoU of 64.15%. Notably, the model was perfected in detecting narrower and more complex canals. These findings highlight the potential of advanced deep learning models for accurate large-scale mapping of irrigation canals, providing valuable insights for researchers, policymakers and water resource managers in improving irrigation network monitoring and development.

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