M-AFDE-NET: NOVEL DEEP LEARNING-BASED BUILDING CHANGE DETECTION OF FRESHLY BUILT LOCALES FROM SATELLITE IMAGERY IN THE NILE VALLEY, EGYPT

Abstract

Urban land expansion is a defining characteristic of urbanization, necessitating the monitoring of this phenomenon and the detection of changes to promote sustainable land use and contribute to updating geospatial databases. Methods based on detecting changes in high-resolution satellite imagery have shown poor performance due to downsampling during image processing, resulting in the loss of boundary information. Furthermore, these methods struggle with complex backgrounds where the ground resembles building roofs. This paper delves into the investigation and evaluation of Freshly Built Locales (FBLs) using bi-temporal images through recently proposed computer vision networks. To address the limitations of existing approaches, we have introduced modifications to the AFDE-Net model, which include the novel residual pyramid attention fusion (RPAF) module. This enhancement enables more precise identification of intricate details in complex change detection scenarios. Our proposed model, M-AFDE-Net, has been evaluated on a newly captured dataset from the Nile Valley regions of Egypt, with a spatial resolution of 30 cm. Special attention has been given to New Mansoura and New Tiba as focal areas for analysis. The evaluation results reveal that the modified model, M-AFDE-Net, outperforms other state-of-the-art models in detecting FBLs. It achieves an impressive F1-score of approximately 89.2%, demonstrating its superiority and effectiveness.