Recent Advances in Urban Expansion Monitoring Through Deep Learning-Based Semantic Change Detection Techniques From Satellite Imagery

Urban expansion monitoring is essential for understanding and managing the dynamic growth of cities. Recently, deep learning (DL)-based semantic change detection (SCD) techniques have emerged as powerful tools for accurately monitoring urban expansion using satellite imagery. This paper offers comprehensive overview of the recent advancements in urban expansion monitoring through DL-based SCD techniques. It covers various publicly available SCD datasets and assesses performance, advantages, and limitations of existing DL-based SCD architectures, categorized into three types. Furthermore, the paper discusses the challenges encountered in DL-based SCD techniques. Finally, it outlines future research directions in urban expansion monitoring using DL-based SCD techniques.