enhancing privacy by large mask inpainting and fusion-based segmentation in street view imagery

Protecting privacy in street view imagery is a critical challenge in urban analytics, requiring comprehensive and scalable solutions beyond localized obfuscation techniques such as face or license plate blurring. to address this, we propose a novel framework that automatically detects and removes sensitive objects, such as pedestrians and vehicles, ensuring robust privacy preservation while maintaining the visual integrity of the images. our approach integrates semantic segmentation with 2d priors and multimodal data from cameras and lidar to achieve precise object detection in complex urban scenes. detected regions are seamlessly filled using a large-mask inpainting technique based on fast fourier convolutions (ffc), enabling efficient generalization to high-resolution imagery. evaluated on the semantickitti dataset, our method achieves a mean intersection over union (miou) of 64.9%, surpassing state-of-the-art benchmarks. despite its reliance on accurate sensor calibration and multimodal data availability, the proposed framework offers a scalable solution for privacy-sensitive applications such as urban mapping, and virtual tourism, delivering high-quality anonymized imagery with minimal artifacts.