Drones in Field Research
Dr Jens Klump1, Dr Tim Brown2, Dr Steve Quenette3
1CSIRO Mineral Resources, Kensington, Australia
2Australian National University, Canberra, Australia
3Monash University, Melbourne, Australia
Small Uncrewed 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 nascent technology, however, there are currently no industry-wide accepted best practices for drone sensor and in-flight data 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, which would ensure that the data meets FAIR data principles. Similarly, software relevant to sUAS used in field research is difficult to find and reuse.
The BoF will provide an overview of Australia’s Scaleable Drone Cloud (ASDC) and early findings from 20 use case expressions of interest. We wish to road test these learnings and explore cases not already captured. The aim of this “Birds of a Feather” (BoF) session is to explore and discuss best practices for the handling of drone-captured data and associated software, and to bring together the sUAS user and developer communities.
Biography:
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.