In scenarios with incomplete or no prior terrain information, the integration of terrain construction and dynamic tracking can be extended by leveraging adaptive algorithms and real-time data collection techniques. One approach is to develop hybrid models that combine statistical terrain feature parameters with real-time terrain data to enhance the accuracy of blockage probability estimation. By dynamically updating the terrain model based on real-time feedback from UAV sensors and user positions, the algorithm can adapt to changing environmental conditions and optimize deployment decisions. Additionally, incorporating machine learning algorithms for terrain reconstruction and predictive modeling can help fill in the gaps in incomplete terrain information and improve the algorithm's decision-making capabilities. Furthermore, integrating advanced sensor technologies, such as LiDAR and radar, can enable UAVs to gather terrain information autonomously in scenarios with no prior terrain data. By combining adaptive algorithms, machine learning, and advanced sensor technologies, the integration of terrain construction and dynamic tracking can be extended to scenarios with incomplete or no prior terrain information, enhancing the overall performance and reliability of UAV networks.