Paper Title
Holosar: A Deep Learning Pipeline For Semantic Segmentation, 3D Reconstruction, And Biometric Motion Analysis

Abstract
In this paper, we develop a complete deep learning pipeline that enables urban scene understanding along with the analysis of biometric micro-movements from Synthetic Aperture Radar (SAR) imagery. This novel pipeline combines three sophisticated algorithms for semantic segmentation, photorealistic 3D reconstruction and fine-grain micro-movement detection amongst complex structures and biometric movements. The most significant contributions are using ASA-DRNet for segmentation; photogrammatic reconstruction using 3D Gaussian Splatting; and analyzing micro-movement which utilizes ConvLSTM. Evaluation on a variety of SAR datasets shows that the proposed pipeline provides significant improvements over existing baselines in terms of better Intersection over Union (IoU) in segmentation, lower root mean square error (RMSE) in 3D reconstruction and greater F1-score in micro-movement detection tasks. The results demonstrate the pipeline's versatility for analysis of urban structures and monitoring acts of biometrics, notwithstanding any limitations imposed by the noise associated with SAR imagery. Our findings suggest that a suite of contemporary and high-performing deep learning architectures could be integrated well into existing SAR-based mapping and biometric analysis applications, providing a new level of accuracy and utility to user's broad range of off-the-shelf and emerging remote sensing methods. Keywords- Synthetic Aperture Radar (SAR), Urban Scene Understanding, Semantic Segmentation, 3D Gaussian Splatting, ConvLSTM, Micro-movement Detection, Biometric Analysis, Remote Sensing.