LAUSR

For decades, displacement detection based on global navigation satellite system (GNSS) has increasingly been an important part of deformation monitoring for applications, such as dams, bridges, and high-rise buildings. Automatic identification and extraction of 3-D displacements from GNSS kinematic positioning can provide a basis for emergency response decision-making and play a crucial role in natural and secondary disasters. However, due to the limitation of single epoch positioning accuracy, automatic detection of displacement from GNSS kinematic positioning results is still a challenge. To resolve this, we propose an enhanced $K$ -means clustering method to detect displacements from GNSS kinematic positioning, which identifies the displacement by clustering and obtains displacements from adjacent clusters. Results from simulation and field experiments have demonstrated the effectiveness of the proposed method. The accuracy of 3-D displacement extraction from GNSS real-time kinematic (RTK) positioning can reach millimeter level.