High-Resolution Remote Sensing Terrain Reconstruction Supported by Three-Dimensional Convolutional Neural Networks

High-resolution remote sensing terrain reconstruction is a critical application area for geospatial modeling, topographic analysis, and environmental monitoring. Interpolation-based techniques and classical 2D convolutional models struggle to capture the complex elevation structures due to a lack of contextual information and inadequate volumetric modeling. To mitigate this limitation, this paper presents a new deep learning architecture called TRIDENT (Terrain Reconstruction via Intelligent Deep 3D Networks) that employs Three-Dimensional Convolutional Neural Networks (3D-CNNs) for 3D terrain reconstruction from high-resolution remote sensing imagery. TRIDENT integrates multi-scale input fusion and dense voxel-wise encoding to achieve better learning of spatial-spectral relationships and acceptable elevation variation. Experimental evaluations on standard datasets show that TRIDENT exhibits a 17.6% reduction in RMSE and SSIM improvement of 12.3% over state-of-the-art 2D-CNN and standard surface reconstruction techniques. The outcomes validate the improved performance of TRIDENT in reconstructing correct and high-resolution digital elevation models (DEMs). The model has tremendous potential in mission-critical applications, such as disaster response planning, precision agriculture, urban development and planning, and detecting land-use alterations. With the integration of volumetric learning and remote sensing information, TRIDENT advances the art of terrain modeling with a technically valid and scalable solution.