As drone data collection becomes standard practice across surveying, engineering, and infrastructure projects, the ability to merge UAV datasets with mobile mapping, terrestrial lidar, ground-penetrating radar, and other capture methods is becoming an increasingly critical, but still complex, professional skill. This session examines that challenge through the lens of real-world projects.
Presentations draw on hands-on experience integrating UAV and mobile mapping data on large-scale DOT road projects, as well as case studies from a growing sUAS program that expanded from aerial imagery into topographic lidar and ultimately into multi-source data fusion to meet demanding project requirements. Both presentations address the processing considerations, workflow decisions, and hard-won lessons that come with combining datasets that weren’t designed to work together. For drone surveyors and geospatial professionals navigating increasingly complex project requirements, this session offers grounded, practical perspective on one of the field’s growing challenges.
The following presentations will be shared in this session:
Case Studies of Data Fusion with sUAS Lidar, Terrestrial Lidar, and Ground Penetrating Radar – U.S. Army Corps of Engineers Mobile District
Presented by David White, I-ATLAS USACE
In 2019, the U.S. Army Corps of Engineers (USACE) Mobile District initiated their small Unmanned Aircraft Systems (sUAS) Program. The sUAS Program originally gained traction by collecting aerial imagery and videos but quickly expanded its capabilities to include structure-from-motion and topographic lidar. Data collected is utilized by multiple divisions within the Mobile District from public affairs, engineering, construction, and operations. As the program grew with sensors and applications, we quickly realized that there was a need to combine data to meet the requirements. This presentation will look at case studies of these UAS applications where data streams (traditional airborne lidar, ground penetrating radar, and terrestrial lidar scanner) were merged to create data fusion products. Combining these datasets overcomes the shortcomings of each individual data type to fulfil the requirements. The presentation will address data processing considerations for past and future data fusion projects, as well as successes and struggles involved with each.
