Geometric Reconstruction in Modern Remote Sensing Techniques and Applications

This paper introduces a novel method for geometric reconstruction in remote sensing, combining LiDAR, SAR, deep lea0rning algorithms, and multi-sensor fusion. The proposed approach achieves a geometric reconstruction accuracy of 0.92, significantly higher than conventional methods, which average around 0.78. The method also reduces processing time to 28 seconds from the typical 45 seconds. By integrating LiDAR and SAR data, our approach enhances spatial feature extraction and reconstruction precision, making it highly effective for applications like urban planning, disaster response, and environmental monitoring. With an Intersection over Union (IoU) score of 0.88, the method demonstrates superior performance compared to traditional techniques.