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.请注意，本Zenodo记录目前处于暂存禁公开期（embargo），相关文件尚未对外开放。待配套学术论文正式发表后，本数据集将正式公开。若有相关需求，可根据具体情况评估并授予访问权限。 AI增强型合成孔径雷达（Synthetic Aperture Radar，SAR）外力作用下地面形变数据集是一套全面的数据集，汇编了关于利用合成孔径雷达（SAR）技术与人工智能（Artificial Intelligence，AI）算法监测外力作用下地面形变的文献计量与系统综述数据。 📁 数据集内容： Bibliometric_Analysis_Results.xlsx → 文献计量分析的详细输出结果 Bibliometrics_Data.zip → 二级文献计量数据集，涵盖作者产出量、被引统计及内容分析（如标题、摘要与关键词长度） Data_Raw.zip → 从Web of Science（WOS）与Scopus数据库获取的原始数据集 DOI_Algorithms_Metrics.xlsx → 62篇纳入综述文献中所使用的AI算法的对比性能数据与成功度量指标（如R²、均方根误差（Root Mean Square Error，RMSE）、平均绝对误差（Mean Absolute Error，MAE）、曲线下面积（Area Under Curve，AUC）） DOI_External_Forces.xlsx → 62篇纳入综述文献中所建模的外力分类（构造、水文、人为、气候等） DOI_Weaknesses_Suggestions.xlsx → 62篇纳入综述研究的作者所报告的方法学局限性与未来研究建议 本数据集为旨在全面评估1984年至2024年间基于SAR与AI的形变监测相关文献的研究人员提供了极具价值的参考资源。