Articles | Volume 18, issue 19
https://doi.org/10.5194/gmd-18-6903-2025
https://doi.org/10.5194/gmd-18-6903-2025
Development and technical paper
 | 
08 Oct 2025
Development and technical paper |  | 08 Oct 2025

Enhanced land subsidence interpolation through a hybrid deep convolutional neural network and InSAR time series

Zahra Azarm, Hamid Mehrabi, and Saeed Nadi

Model code and software

Enhanced Land Subsidence Interpolation through a Hybrid Deep Convolutional Neural Network and InSAR Time Series Z. Azarm https://doi.org/10.5281/zenodo.12721120

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Short summary
The article introduces a new method to estimate land subsidence using deep convolutional neural networks (CNNs) and persistent scatterer interferometric synthetic aperture radar (PSInSAR), addressing the limitations of traditional methods. It focuses on Isfahan Province, Iran, and demonstrates substantial improvement over conventional techniques. The deep CNN method showed a 70 % enhancement in subsidence prediction, with the study area experiencing over 38 cm of subsidence between 2014 and 2020.
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