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

## TL;DR

This paper explores how to make drone data more shareable and reusable by identifying key metadata requirements for better scientific use.

## Contribution

The study identifies essential metadata requirements for UAV data to support FAIR principles, based on dataset analysis and user feedback.

## Key findings

- Current UAV metadata often lacks details on sensors, processing, and licensing.
- Users and experts emphasize the need for standardized metadata across the UAV data lifecycle.
- Improved metadata practices can enhance data sharing and reuse in scientific research.

## Abstract

Uncrewed Aerial Vehicles (UAVs), or drones, are increasingly used in research areas such as precision agriculture, environmental monitoring, and disaster response. They can carry a wide range of sensors and users produce diverse datasets that include raw imagery, orthomosaics, and spatial models. Sharing these datasets in a Findable, Accessible, Interoperable, and Reusable (FAIR) way requires rich metadata. However, current practices often lack essential details about sensors, processing steps, and licensing, which limits FAIR compliance. We assessed how UAV data is currently published by reviewing metadata from 71 datasets in public repositories. We also evaluated existing metadata frameworks and surveyed over 70 UAV data users and experts to understand their needs and challenges. Based on this analysis, we identify key metadata requirements across the UAV data lifecycle, including information on sensors, spatial and temporal coverage, processing workflows, and provenance. Our goal is to clarify and summarize these needs rather than propose a formal standard. More consistent metadata practices will support the FAIR principles and improve UAV data sharing and reuse in science.