## 6 Conclusions  
Although multi-source remote-sensing techniques have been widely applied to landslide hazard identification, research on concealed landslide hazards in areas characterized by loess cover, complex terrain, and limited field data remains insufficient. To address this gap, this study focuses on the central region of Tianshui City, Gansu Province, and proposes a comprehensive remote-sensing identification approach that integrates InSAR-based deformation monitoring with optical remote-sensing interpretation. By comparing \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Characteristic Factor**} & \multirow{2}{*}{**Binned Column**} & \multicolumn{2}{c}{**Number of**} & \multicolumn{2}{c}{**Characteristic Factor**} & \multicolumn{2}{c}{**Binned Column**} & \multicolumn{2}{c}{**Number of LANDSLIDE**} \\  & & **Landside Hazards** & & & & **Hazards** \\ \hline \multirow{8}{*}{Slope} & 0–5 & 1 & & North & 24 \\  & 5–15 & 55 & & Northeast & 19 \\  & 15–25 & 52 & & East & 21 \\  & 25–35 & 17 & Aspect & Southeast & 9 \\  & 35–45 & 2 & & Southwest & 4 \\  & \textgreater{}45 & 0 & & West & 11 \\  & & & & Northwest & 31 \\ \hline \multirow{2}{*}{Distance from River Channels} & 0–250 m & 19 & \multirow{2}{*}{Distance from Fault} & 0–1000 m & 16 \\  & 250–500 m & 23 & & Lines & 1000–2000 m & 21 \\  & \textgreater{}500 m & 85 & & \textgreater{}2000 m & 90 \\ \hline \multirow{8}{*}{Lithology} & Hard rock & 2 & \multirow{8}{*}{Deformation data} & \textgreater{}25 mm/a & 45 \\  & Weaker rock & 45 & & 15–25 mm/a & 36 \\ \cline{1-1}  & Collapsible loess & 63 & (Values are & 5–15 mm/a & 24 \\ \cline{1-1}  & Harder rock & 17 & absolute) & 0–5 mm/a & 22 \\ \cline{1-1}  & & & & 5–15 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary table of spatial characteristics of potential landslide hazard points. multi-source interpretation results and validating them using high-resolution Google Earth imagery, a total of 146 potential landslide hazard points were initially identified. After careful correction and validation, 127 points were confirmed, resulting in a relatively complete landslide hazard inventory. The main findings of this study include: (1) using this integrated remote-sensing method, a total of 127 concealed landslide hazard points were confirmed, with a validation accuracy of 86.99% based on Google Earth imagery; (2) InSAR and optical remote-sensing techniques are complementary: InSAR is suitable for detecting slow-moving or latent landslides, while optical imagery excels at identifying morphological features of occurred landslides; (3) landslide hazards in the study area exhibit significant hydrological driving characteristics, primarily triggered by river erosion or intense rainfall, and are closely related to the widespread distribution of loose loess strata; and (4) the proposed method significantly improves both the accuracy and spatial coverage of hazard identification, effectively addressing the limitations of single-source remote sensing. One limitation of this study lies in the absence of large-scale field monitoring data, which may hinder the identification of landslides in areas with dense vegetation or image shadows. Nevertheless, the general consistency between InSAR and optical remote-sensing results, supported by terrain and geological analysis, demonstrates the feasibility of this approach for early landslide identification and hazard assessment. The findings hold important engineering significance and provide technical support for early warning, disaster prevention, and spatial planning in landslide-prone areas of the Loess Plateau. Future work should further integrate deformation time-series analysis and real-time rainfall data to enhance the predictive capacity and intelligence level of landslide monitoring systems. Methodology, L.Z. ; Validation, T.Y. ; Investigation, Y.X. ; Data curation, Y.Z. ; Writing--original draft, J.M. ; Writing--review & editing, Q.S. All authors have read and agreed to the published version of the manuscript.