Large-Scale Privacy Redaction for Geospatial Imagery
Design a machine learning system to automatically detect and blur sensitive PII, specifically faces and license plates, in a global corpus of billions of high-resolution 360-degree street-level images. The system must prioritize near-perfect recall for privacy compliance while maintaining high precision to preserve the utility of the maps. Your design should detail a distributed batch-processing architecture, handle geometric distortions in spherical imagery, and explain how to manage global variations in license plate formats and hard negatives like statues or billboards. Address the trade-offs between processing throughput, inference cost, and model accuracy at petabyte scale.
YOLOv8Apache BeamFeature Pyramid NetworkTensorRTQuantizationActive LearningInpaintingGaussian BlurCNN
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