# Two Novel Multidimensional Data Analysis Approaches Using InSAR Products for Landslide Prone Areas
###### Abstract  
Successfully detecting ground deformation, especially landslides, using InSAR has not always been possible. Improvements to existing InSAR tools are needed to address this issue. This study develops and evaluates two novel approaches that use multidimensional InSAR products to detect surface displacements in the landslide-prone region of Buyukalan, Antalya. Multi-temporal InSAR analysis of Sentinel-1 data (2015-2020) is performed using LiCSAR-LiCSBAS, followed by two novel approaches: multi-dimensional InSAR research and analysis (MIRA) and Crosta's InSAR application (InCROSS). Cumulative LOS velocity maps reveal deformation rates of \(-1.1\) cm/year to \(1.0\) cm/year for descending tracks and \(-3.8\) cm/year to \(3.8\) cm/year for ascending tracks. Vertical displacements range from \(-1.9\) cm/year to \(2.3\) cm/year and east-west components from \(-2.8\) cm/year to \(2.9\) cm/year. MIRA uses an n-Dimensional Visualizer and SVM classifier to identify deformation clusters, and InCROSS applies PCA to enhance deformation features. MIRA increases the deformation detection capacity compared to conventional InSAR products, and InCROSS integrates these products. A comparison of the results reveals \(80.48\%\) consistency between them. Overall, the integration of InSAR with statistical and multidimensional analysis significantly enhances the detection and interpretation of ground deformation patterns in landslide-prone areas. precise monitoring of ground movements, thereby contributing to the mitigation of loss of life and property through the implementation of early warning systems [6; 7]. InSAR-based methodologies, particularly the Small Baseline Subset (SBAS) and multi-track time-series analyses, have been increasingly applied to monitor and assess landslide dynamics due to their ability to detect millimeter-level surface deformations over large areas. These methods offer significant advantages in terms of temporal continuity, spatial coverage, and the ability to integrate ascending and descending orbit data for improved displacement decomposition. Recent studies have utilized SBAS-InSAR, multi-orbit fusion approaches, and AI-enhanced interferometric techniques to track pre-failure signals, to analyze post-failure behavior, and to evaluate rainfall-induced activation mechanisms. The versatility of these techniques has been demonstrated in various environments, including mountainous terrain, open-pit mines, glacial lake basins, and infrastructure corridors, highlighting their robustness under diverse geomorphological and climatic conditions [8; 9; 10; 11; 12; 13]. The western Antalya region is highly susceptible to landslides due to its geographical and geological characteristics. The primary natural factors contributing to landslide occurrence in this area include steep topography, a complex geological structure (particularly fault zones where units, such as limestone, schist, and claystone coexist), and intense seasonal precipitation [14]. For example, the Yesil Gol landslide, which developed on the eastern slope of the Akdag Massif in the Teke Region, was examined through detailed geomorphological analysis. It was identified as a planar sliding event that occurred along a vertical fault plane at the contact zone between limestone-conglomerate and claystone units [15]. This finding indicates that most deep-seated landslides in the region tend to develop along weak planes between geological layers. Additionally, a temperature and precipitation graph based on data from the Antalya meteorological station was generated, revealing a correlation between periods of high rainfall and increased landslide activity, as was also discussed in [14]. In recent years, the use of remote sensing technologies for the detection, monitoring, and mapping of landslides has become prominent in studies conducted in western Antalya.