  Deep learning (DL) algorithms are considered as a methodology of choice for
remote-sensing image analysis over the past few years. Due to its effective
applications, deep learning has also been introduced for automatic change
detection and achieved great success. The present study attempts to provide a
comprehensive review and a meta-analysis of the recent progress in this
subfield. Specifically, we first introduce the fundamentals of deep learning
methods which arefrequently adopted for change detection. Secondly, we present
the details of the meta-analysis conducted to examine the status of change
detection DL studies. Then, we focus on deep learning-based change detection
methodologies for remote sensing images by giving a general overview of the
existing methods. Specifically, these deep learning-based methods were
classified into three groups; fully supervised learning-based methods, fully
unsupervised learning-based methods and transfer learning-based techniques. As
a result of these investigations, promising new directions were identified for
future research. This study will contribute in several ways to our
understanding of deep learning for change detection and will provide a basis
for further research.
