Australia’s Scalable Drone Cloud

Establishing a national ecosystem for drone data management, integrating sensing capabilities with easy-to-use storage, processing, visualisation and data analysis tools (including computer vision and deep learning techniques), and transforming research-led data and tools contributions into a drones data commons.

Unmanned Aerial Systems or Vehicles (UAS), commonly known as drones, provide sensing capabilities that address the critical scale-gap between ground and satellite-based observations. Drone operations capture large amounts of data, far beyond an individual’s capability to manage without tools and systems to support the coordination of storage, information exchange, exploration and processing.

The Establishing Australia’s Scalable Drone Cloud (ASDC) project will establish a national ecosystem for drone data management operating on FAIR principles. This will offer a competitive advantage for researchers through the ability to deliver near real-time societally-relevant information from live drone-acquired data.

Start date 1 February 2020

Expected completion date 30 March 2023

Investment by ARDC $855,000

Co-investment partners

Monash eResearch Centre

AuScope

TERN

CSIRO

Australian Plant Phenomics Facility

Lead node

1 Advanced Drones Virtual Laboratory (DronesVL)

A friction-free analysis environment for advanced bespoke processing relevant to any point of the drones-data-lifecycle from instrument capture to domain knowledge.

2 MyTardis sensor data store (DronesMT)

Establish the operations of MyTardis for drone data on the Research Cloud. MyTardis integrates with scientific instruments, instrument facilities and research storage and computing infrastructure to address the challenges of data storage, data access, collaboration and data publication.

3 Spectral Drone Cloud (SpectralDC)

Cloud-native drone data management and analytics capability for different imaging systems including hyperspectral, multispectral and RGB.

4 Essential tools for working with point clouds (PointDC)

Scalable containerized tools to enable upload, download, and the provision of persistent links for easy sharing of datasets.Visualisation tools including interactivity with point cloud data in Virtual Reality (VR)/Augmented Reality (AR).

5 Processing pipelines

Development of best-practice drone data processing pipelines for five different use cases, that can be reused by multiple research communities, promoting data standardisation.

Core features

Built by the community

The community-driven open ASDC will minimise the need for the development of bespoke and silo solutions in drone data management.

Social architecture

Will enable the progression from siloed technical systems to a connected national ecosystem of increasingly FAIR data and accelerated access to data use.

Transformative

Drone data has a transformative impact on agriculture and environmental monitoring and management. More precise data collection and improvements in infrastructure bring cloud-backed processing and analytical capabilities (e.g. machine learning and Artificial Intelligence) to the field, to provide near real-time decision support.

Who is this project for?

Researchers
Farmers and industry
Environmental managers
Government (state and commonwealth)

What does this project enable?

The ASDC will enable Australian research to move towards a common platform to manage and access drone data, reuse processing pipelines, implement best practice information architecture with cloud-native and HPC solutions, and collaborate with the international community at a platform level instead of individual domain level. The ASDC will also enable wider use of research using drone data by multiple research communities.

Handy resources

Try Drone mapping software, OpenDroneMap.org
Utilise ecocloud for cloud based computing tailored to ecological data and researchers.
Explore Monash University’s Drone Discovery Platform (MDDP)
Visit the Adelaide University Unmanned Research Aircraft Facility (URAF)

Other groups and people working with drone, LiDAR and near remote sensing data: