<p>Real-time monitoring of terrain deformation is crucial for predicting geohazards and ensuring the safety of vulnerable areas. This study presents a feature-based approach specifically developed for real-time deformation analysis, focusing on detecting and tracking features on hillshade models derived from point clouds. The method identifies regions of interest by integrating contour line analysis and feature detection, enhancing computational efficiency while maintaining accuracy. The method was tested on a controlled laboratory setup and the Hochebenkar rock glacier landslide in the Eastern Austrian Alps. The laboratory setup provided an ideal environment to validate the method’s precision, achieving sub-millimeter accuracy under controlled and optimal conditions. The Hochebenkar dataset, with its challenging natural terrain and variable textures, demonstrated the method’s robustness in identifying and analyzing deformation patterns. Among the tested feature-based approaches, the combination of KAZE and ORB feature detection and matching algorithms emerged as the most effective, balancing feature detection quality and computational speed. Contour lines were instrumental in isolating regions with high deformation, streamlining the analysis process. The results showed that the method is well-suited for real-time monitoring, accurately detecting the deformation’s magnitude and direction. For example, in the Hochebenkar dataset, a deformation of 19.1&#xa0;cm over 13 days was identified. At the same time, the method also revealed areas where texture changes limited feature matching. These findings emphasize the method’s potential for real-time applications in geohazard monitoring and early warning systems. Future work will focus on further optimizing the method to handle sudden texture changes and scaling it for larger datasets.</p>