Research on the Method of Using Deep Learning to Improve the Accuracy of Complex and Mature Spatial 3D Remote Sensing Reconstruction

Driven by the construction of “Digital China” and advances in spatial information technology, three-dimensional remote sensing reconstruction of complex environments has become increasingly important for digital twin applications and intelligent spatial analysis. In particular, the integration of electromagnetic remote sensing modalities, including synthetic aperture radar and LiDAR, provides complementary information for high-precision environmental perception and large-scale scene reconstruction. However, complex terrains generally suffer from variable illumination, severe occlusion, heterogeneous land-cover distribution, and sensor noise interference, resulting in insufficient reconstruction accuracy, poor detail preservation, geometric distortion, and limited robustness in conventional methods. To address these challenges, this paper proposes a deep learning-based approach for improving the accuracy of complex spatial 3D remote sensing reconstruction by establishing a unified framework that integrates multimodal data fusion with adaptive noise suppression and collaborative geometric correction. The proposed method enhances the complementary utilization of heterogeneous sensing information and optimizes reconstruction precision in largescale complex scenes, providing effective technical support for intelligent spatial perception and electromagnetic remote sensing applications.