Object classification, change detection, and anomaly detection in LiDAR and other point cloud datasets.
Description
Leveraging AI for point cloud analysis may advance accuracy and efficiency of: •Point cloud classification, enhancing capabilities for LiDAR based autonomy and navigation. •Change detection, enriching scientific investigations of geomorphology and surface processes. •Anomaly detection, enhancing inspection of hardware and monitoring of manufacturing processes.
Detailed example
Leveraging AI for point cloud analysis may advance accuracy and efficiency of: • Point cloud classification, enhancing capabilities for LiDAR based autonomy and navigation. • Change detection, enriching scientific investigations of geomorphology and surface processes. • Anomaly detection, enhancing inspection of hardware and monitoring of manufacturing processes.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
Leveraging AI for point cloud analysis may advance accuracy and efficiency of: •Point cloud classification, enhancing capabilities for LiDAR based autonomy and navigation. •Change detection, enriching scientific investigations of geomorphology and surface processes. •Anomaly detection, enhancing inspection of hardware and monitoring of manufacturing processes.
Controls / human review
ATO: Not reported; PIA: Not published