Dr Jens Klump1, Dr Steve Quenette2, Dr Jane Wyngaard3
1CSIRO, Kensington, Australia, 2Monash University, Melbourne, Australia, 3University of Notre Dame, Notre Dame, USA
Small Unmanned Aerial Vehicles (sUAS), commonly known as drones, are being routinely applied to capture data in numerous scientific applications. The capabilities of drone-mounted sensors address the critical scale gap between ground and satellite-based observations by collecting high-resolution data (mm-cm scale) over large surface areas (1–10 km² and greater). Drone campaigns capture large amounts of data. Moreover, the competitive advantage is the ability to deliver near-real-time society-relevant information.
As a new technology, however, there are currently no industry-wide accepted best practices for drone sensor and flight data handling and management. Without common standards, the development of mature tools for drone-captured data processing and fusion with other data sources is currently hampered. As a consequence, each use case generally develops a unique custom pipeline that only sees one-time use. Furthermore, drone-captured data is – for the most part – not being managed according to data stewardship best practices, such as would ensure the data is FAIR data principles.
The aim of this BoF is to explore and discuss best practices for the handling of drone-captured sensor and flight data. The co-location with the RDA Plenary also gives us the opportunity to bring together the Australian and international user and developer communities.
Jens Klump is a geochemist by training and leads the Geoscience Analytics Team in CSIRO Mineral Resources based in Perth, Western Australia. In his work on data infrastructures, Jens covers the entire chain of digital value creation from data acquisition to data analysis with a focus on data in minerals exploration. This includes automated data and metadata capture, sensor data integration, both in the field and in the laboratory, data processing workflows, and data provenance, but also data analysis by statistical methods, machine learning and numerical modelling.