AI-Augmented SAR for Ground Deformation Under External Forces Datasets

Please note that the Zenodo record is currently under embargo and the files are not yet publicly accessible. It will be made publicly available once the scientific article is published. Upon your request, access permission can be evaluated and granted accordingly. AI-Augmented SAR for Ground Deformation Under External Forces Datasets is a comprehensive dataset that compiles bibliometric and systematic literature review data on the monitoring of ground deformation under the influence of external forces using Synthetic Aperture Radar (SAR) technologies and Artificial Intelligence (AI) algorithms. 📁 Dataset Contents: Bibliometric_Analysis_Results.xlsx→ Detailed outputs of the bibliometric analysis. Bibliometrics_Data.zip→ Sub-level bibliometric data including author productivity, citation statistics, and content analyses (e.g., title, abstract, and keyword lengths). Data_Raw.zip→ Raw datasets retrieved from Web of Science (WOS) and Scopus databases. DOI_Algorithms_Metrics.xlsx→ Comparative performance data and success metrics (e.g., R², RMSE, MAE, AUC) of AI algorithms used in the 62 reviewed articles. DOI_External_Forces.xlsx→ Classification of external forces (tectonic, hydrological, anthropogenic, climatic, etc.) modeled in the 62 reviewed articles. DOI_Weaknesses_Suggestions.xlsx→ Methodological limitations and future research suggestions reported by the authors in the 62 reviewed studies. This dataset provides a valuable resource for researchers aiming to conduct a comprehensive assessment of SAR+AI-based deformation monitoring literature spanning the years 1984 to 2024.