Landslides are triggered by various factors, including seismic activity, climate-related events, and gravitational forces. These events pose significant risks to life, property, and the environment, necessitating effective monitoring and quantification for mitigation and prevention. Traditional monitoring methods like in-situ sensors face limitations in cost, scalability, and real-time data processing. In the realm of landslide and hazard mitigation, time is of the essence because the quicker data is processed, the sooner policymakers and emergency responders can act to protect lives and safeguard economic infrastructure. The urgency and the critical role of rapid, real-time data processing have inspired us to expand and further develop a novel open-source package called AkhDefo (Akh: Land in Kurdish language and Defo: Deformation in English Language) ( https://pypi.org/project/akhdefo-functions/ ). This study introduces new features to AkhDefo, transforming it from an open-source code into a standalone geospatial python library. These enhancements include optical flow algorithms for measuring displacement using satellite radar backscatter, optical images, and real-time live stream camera data from ground-based sources. The satellite radar and optical images were processed to derive volume estimates and study kinematic behavior in the May 2017 Mud Creek landslide in California, USA, and the Morenny rock-glacier in the Tien Shan Mountains, Kazakhstan between 2017 to 2023. In addition, live-stream webcam data were used to investigate a rockfall event on the September 20, 2021, at Stawamus Chief in Squamish, British Columbia, Canada, and from this, developed a state-of-the-art rock-fall detection system.