遥感变化检测可以获取地表变化信息，对于理解人与自然相互作用，推动可持续发展具有重要意义。随着遥感成像技术的提升和计算机科学的快速发展，高光谱、高时间、高空间分辨率的遥感影像已广泛应用，促进了深度学习的遥感变化检测发展以及多领域成功应用。与传统遥感变化检测不同，基于深度学习的变化检测提取遥感影像的深度差异特征，无需构建特征工程，检测精度和效率均有所提高。本文结合文献计量学全面分析本领域研究现状和热点，发现基于深度学习的变化检测在国内机构学者的主导下快速发展并取得了大量研究成果。这些成果大都基于高分辨率图像和CNN网络，并成功应用于土地利用/覆盖和建筑变化检测等。在此基础上，从像素、对象和场景三个粒度对基于深度学习的遥感变化检测方法分类介绍，阐述开展像素、对象和场景的特征提取以及后续网络分析过程，其中基于对象和场景的方法具有优势。最后，归纳总结目前面临的挑战及未来可能发展方向。由于遥感平台的发展和应用需求的增加，多模态异质变化检测是未来发展趋势。另外，深度学习的方法还需要克服非理想样本问题，关注多元变化信息获取，以及推进变化检测的广泛应用等。

Remote sensing change detection can provide information on land surface change, which is important for studying man-nature interactions and facilitating sustainable development. With the advancement of remote sensing imaging technology and the rapid development of computer technology, extensive remote sensing images with various modes and spectral, spatial, and temporal resolutions have been collected, allowing for the development of massive remote sensing change detection methods based on deep learning and their successful application in a wide range of fields. Unlike previous reviews, this work examines remote sensing change detection based on deep learning from the perspectives of bibliometric analysis, research scale, and critical problem exploration, with the goal of providing reference materials for future remote sensing change detection research. The definition and importance of remote sensing change detection, as well as the motivation for this review, are briefly presented in the introduction. The literature structure and research hotspot information of existing research, such as the number of publications, distribution of journals and institutions, main researchers, common data sources, network model, and application field information, are clarified in the second section, which is combined with bibliometric analysis. The third section focuses on deep learning-based remote sensing change detection algorithms, which are categorized and presented on three scales: pixel, object, and scene. It explains how to extract pixels, objects, and scenes from remote sensing images, as well as how to perform network analysis. The fourth section covers the limitations of deep learning-based remote sensing change detection and offers the most recent research to address these issues as well as future development possibilities. After then, there is a segment dedicated to the finale. The bibliometric analysis reveals that deep learning-based change detection has progressed rapidly in the last three years, with fruitful research results and domestic institutional scholars dominating. High resolution images and CNN are the most used data sources and network model; and extensive land use/coverage and building change detection are hot application fields. As for methods, different research scales respond to varied data features and network model structure, with the object and scene technique having advantages. In the meanwhile, they face similar issues, which are summarized below. First, there"s the problem of detecting changes using multimodal remote sensing data. To address this, adversarial training, attention mechanisms, and feature deep fusion methods based on feature space transformation appear to be promising. Multimodal data fusion and other multimodal learning approaches are among the future"s emerging directions. Second, change detection under small sample and imbalanced sample settings is difficult. Semi-supervised schemes must be improved to address the problem of small sample size, and self-supervised methods are predicted to become a research hotspot. While the oversampling technique and ensemble learning in deep learning models provide a new path for unbalanced samples. The third issue is obtaining diversified change information. Semantic change detection, which obtains extensive information on change types, and Transformer for time series change detection, which obtains long-term change information, are the future trends. Furthermore, deep learning-based change detection requires advances in gathering dynamic information such as time and seasonal pattern of change. To summarize, this work systematically compiles and reviews the research status and progress of deep learning-based remote sensing image change detection. And multimodal heterogeneous change detection, semantic change detection, and time series change detection are future prospects as application needs and data diversity grow. In the areas of resources, the environment, and disaster relief, there are presently few practical uses of existing knowledge. Continuously deepening the in-depth study of new technologies and methods is required, as is promoting wide and in-depth remote sensing change detection research and application.