Accurately labeled data ensures a supervised learning algorithm’s ability to correctly classify new, unseen instances. Currently, sections of a land engraved area scan, represented as 3d topographical data, are annotated by a trained operator. Manually annotating land engraved area scans is a time-consuming process. We propose using trained neural networks to automate the labeling of these land engraved area scans. Using the currently labeled land engraved area scans, we present a data processing pipeline that transforms surface data for multi-classification 3d convolutional neural networks.
Read more →
