Authors: Andrew Lyons*, Univeristy of California, Jacob Flanagan, University of California Division of Ag & Natural Resources, Sean Hogan, University of California Division of Ag & Natural Resources
Topics: UAS / UAV, Drones, Remote Sensing
Keywords: drones, UAS, data management, Pix4D
Session Type: Poster
Start / End Time: 9:55 AM / 11:35 AM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: Download
Modern photogrammetry platforms like Pix4D have dramatically improved the quality and speed of generating orthomosaics from drone images. However a significant amount of data management remains before and after the stitching process. Data management begins well before the drone is launched, and includes organizing mission planning documents, quality control checks in the field, transferring images and flight logs from one storage medium to another, sorting images into individual flights, inspecting and selecting images for processing, launching processing software, generating user-friendly catalogs of project results, delivering data and results to clients, and packaging data for short and long-term archival storage. Flight management apps and cloud based processing platforms facilitate some data management tasks, but not all, and tend not to be highly configurable. Likewise, desktop software leaves much of the data management to the user. We present a toolbox of open source tools we've developed to facilitate data management for drone mapping projects centered around Pix4D desktop as the stitching engine. These include a directory tree template for organizing all project data, Python scripts for transferring and sorting data, and a R package for field data quality inspections and generating image data catalogs. This work is based on three years of experience of collecting drone data for research in agriculture and natural resources in the University of California Division of Agriculture and Natural Resources.