Towards a FAIR metadata framework for drone and uncrewed aerial vehicle data

Area of Science:

• * Remote Sensing and Geospatial Science
• * Environmental Science and Agriculture Technology

Background:

• * Uncrewed Aerial Vehicles (UAVs), or drones, are vital research tools generating diverse datasets for applications like precision agriculture and environmental monitoring.
• * Effective data sharing relies on the Findable, Accessible, Interoperable, and Reusable (FAIR) principles, necessitating comprehensive metadata.
• * Existing metadata practices for UAV data often omit critical information, impeding data discoverability and reuse.

Purpose of the Study:

• * To assess current UAV data publishing practices and identify metadata gaps.
• * To understand the metadata needs and challenges faced by UAV data users and experts.
• * To outline key metadata requirements for enhancing the FAIRness of UAV datasets.

Main Methods:

• * Reviewed metadata from 71 UAV datasets in public repositories.
• * Evaluated existing metadata frameworks relevant to geospatial data.
• * Surveyed over 70 UAV data users and domain experts on their requirements.

Main Results:

• * Current UAV data publications frequently lack essential details regarding sensors, processing workflows, and licensing information.
• * A significant need exists for standardized metadata covering the entire UAV data lifecycle, from acquisition to analysis.
• * Identified key metadata requirements include sensor specifications, spatial/temporal coverage, processing details, and data provenance.

Conclusions:

• * Improved and more consistent metadata practices are crucial for advancing the FAIR principles in UAV research.
• * Addressing identified metadata gaps will enhance the sharing, discoverability, and reusability of valuable UAV-derived scientific data.
• * The study clarifies essential metadata needs to guide future data management strategies for drone-based research.