Jordan Journal of Civil Engineering

Paper Detail

Deep Learning based Land Cover Change Detection in Remote Sensing Imagery

Volume 17, No. 4, 2023
Received: 2022/10/23, Accepted: 2023/07/11


Diana Andrushia; Mishaa Manikandan; Mary Neebha; Anand N; Johnson Alengaram;


Change detection is a paramount method for understanding land cover changes. In remote sensing, satellite images are crucial for determining environmental changes. Land cover data is used to understand better change detection and improvement in urbanization on the Earth's surface. With the significant advancement in deep learning methods and their feature representation, deep learning methods are more prevalent in solving change detection tasks. In this work, an end-to-end encoder-decoder architecture is used to detect the changes in the land cover. The proposed method uses residual U-Net to find land cover images changes. The U-Net structure is used as backbone of the network. The effectiveness of the proposed method is experimented through LEVIR-CD datasets. The results show that the proposed method outperforms the state-of-the-art techniques and gives reliable results.


Change detection, Remote Sensing, Residual U-Net, Deep learning