Ground deformation monitoring is a critical component in understanding natural and anthropogenic processes that impact infrastructure safety, disaster risk, and sustainable resource management. The rapid growth in remote sensing and geodetic measurement techniques over the past four decades has led to a complex and expanding body of research. This study aims to systematically map recent trends, techniques, and research gaps in ground deformation monitoring, addressing the need for a comprehensive synthesis of methodological advances and thematic priorities. A bibliometric analysis was conducted using data from the Web of Science and Scopus databases, covering 3,616 articles published between 1984 and 2024. Data processing included keyword filtering and discipline-based selection to ensure relevance. Results reveal that interferometric synthetic aperture radar and global navigation satellite systems are the most prominent techniques, while machine learning and deep learning approaches have gained significant momentum after 2020. International collaboration rates have reached 33%, with China, the United States, and Italy leading in publication output and impact. The findings demonstrate a shift toward integrated, data-intensive, and interdisciplinary research, emphasizing decision support and risk management applications. These trends highlight the importance of methodological standardization, data fusion, and artificial intelligence for advancing ground deformation monitoring.
Kara et al. (Tue,) studied this question.