Deep Learning-based Land-cover Change Detection in Remote-sensing Imagery

With the significant advancement in deep-learning methods and their feature representation, deep-learning methods are more prevalent in solving change-detection tasks. The prime purpose of change detection is to detect the changes on the surface of the earth. 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 image changes. The UNet structure is used as the backbone of the network. The effectiveness of the proposed method has been experimented through LEVIR-CD datasets. The results showed that the proposed method outperforms the state-of-the-art techniques and gives reliable results. These techniques can be used to examine changes in the earth's crest due to natural events, such as landslides, earthquakes, erosion and geo-hazards or human activity, like mining and development. KEYWORDS: Change detection, Remote sensing, Residual UNet, Deep learning, Land cover, Climate.