Protecting Data Privacy in Landslide Detection Using Privacy-Preserving Machine Learning

Landslides pose substantial risks to both local populations and critical infrastructure in high-risk areas. Numerous technologies have been developed to monitor landslides, resulting in a growing amount of landslide monitoring data, such as very high resolution remote sensing data and in-situ monitoring data. These data have great potential for developing advanced machine learning models for geohazard assessment. Privacy and security issues are raising concerns, hindering the collection of large datasets required for developing powerful machine learning models. However, existing landslide detection models explicitly or implicitly assume that landslide monitoring and mapping data are directly shared on a centralized server. This assumption leads to a gap between data sharing practices and machine learning modeling in landslide detection. To bridge this gap, we leverage a privacy-preserving machine learning model for the landslide detection task. First, a federated learning method is introduced to protect data privacy throughout the modeling process, enabling the development of landalide detection models without the need to share raw data. Second, we introduce a fair incentive mechanism to evaluate the contributions of participants and encourage more data owners to engage in landslide data sharing. Finally, experimental results demonstrate that the proposed framework effectively protects data privacy while maintaining high prediction accuracy. This approach not only facilitates secure data sharing but also enables institutions to develop more robust machine learning models for geohazard assessment, thereby advancing the field of landslide prevention and mitigation.