From Data to DRIVR: connecting sensors in the field with interfaces in the lab using cyclical data ecosystems

Recent advances in spatial sensor technology have significantly enhanced the resolution, fidelity, and quantity of spatial data, driving progress in geovisualization. These trends are further extended by improvements in spatial computing and interface technology, creating new opportunities for immersive data visualization. This thesis explores a remote field data production system that integrates data collection, processing, and interpretation in both field and lab settings. A comprehensive review of 3D geovisualization in the geosciences identifies key limitations and potentials, laying the groundwork for a custom LiDAR system to address these issues. The resource utilization and data fidelity of a vehicle-mounted LiDAR system are assessed, highlighting its potential in remote field data collection. Additionally, a new experiential field data visualization interface (DRIVR) is developed, simulating driving through collected data to bridge field and lab experiences. This research emphasizes efficient data management and immersive visualization to enhance decision-making and collaboration in geospatial applications.