Bi-temporal optical remote sensing image change detection constitutes a pivotal domain within the broader field of Earth observation， aimed at systematically quantifying terrestrial surface dynamics. By conducting comparative analyses of co-registered imagery acquired over identical geographical coordinates at distinct temporal intervals， this methodology facilitates critical applications ranging from urban expansion monitoring and resource management to disaster damage assessment. The exponential expansion of remote sensing data， coupled with the precipitous maturation of deep learning paradigms， has instigated a transformative era for this discipline. Consequently， the field is witnessing a phase of rapid algorithmic iteration and profound evolutionary growth， significantly enhancing the capability to interpret complex spatiotemporal patterns. Against this backdrop， this manuscript employs a comprehensive chronological framework to systematically represent deep learning-based change detection architectures established over the past two decades. Complementing this survey， it rigorously conducts a comparative analysis， explicitly evaluating both the detection accuracy and computational efficiency of these state-of-the-art methodologies across mainstream benchmark datasets. Beyond mere algorithmic review， the paper consolidates widely utilized public datasets and essential evaluation metrics， thereby providing a standardized reference for benchmarking model performance. Furthermore， this study structurally deconstructs the comprehensive change detection pipeline into its fundamental components. Subsequently， the specific technological advancements and methodological innovations driving the evolution of each critical stage are scrutinized in granular detail to illustrate the workflow’s maturation. Ultimately， prospective research frontiers are delineated to forecast the field’s developmental trajectory. This outlook aims to serve as a roadmap， offering essential reference and guidance to steer subsequent investigations and foster continued innovation within the domain.