These are 3D contours from LiDAR point cloud of Las Vegas. The QL-1 datasets (≤10cm vertical/≤35cm horizontal accuracy, ≥8 points/m²) required preprocessing due to excessive data volume (142GB for Santa Clara alone). Our method reduces data while preserving structurally critical line features for satellite image-LiDAR point cloud registration, focusing on building contours rather than less prominent road edges. First, building footprints were extracted using Google's 2D shape vectors instead of raw segmentation or classification.