Change detection refers to the technology of extracting land cover changes by comparing and analyzing multi-temporal remote sensing images acquired at different periods covering the same area. With the advancements in satellite and sensor technologies, there has been a significant increase in Earth observation data. Change detection plays an important role in various fields, including geoinformation survey and ecological environment protection. In recent years, deep learning technology has become an advanced method for change detection due to its powerful feature mining ability. This paper provides a comprehensive overview of deep learning-based change detection methods from three aspects: pixel-level, object-level, and scene-level. Furthermore, this paper discusses the practical implementation of deep learning in change detection through three research examples. Finally, the study concludes by outlining future development trends in deep learning-based change detection.