COLLARIS - Overview of currently used and possible technical solutions for data analysis and data sharing, including common practices: Assessment and recommendations for future use

This deliverable presents an assessment of currently used technical solutions for data analysis and data sharing, with a specific focus on Unmanned Aerial Systems (UAS) data. The aim of this document is to provide an overview of the state of the art in UAS data analysis and sharing, including key practices and recommendations for future implementation.

The first part of this deliverable focuses on UAS data analysis, highlighting its importance in various sectors. The key steps involved in the UAS data analysis process are outlined, emphasising the need for accurate and efficient analysis techniques. An overview of the state-of-the-art solutions for UAS data analysis is then provided. These include image solutions such as image processing and computer vision, photogrammetry, LiDAR data analysis, data fusion, machine learning and artificial intelligence, real-time analytics, and Geographic Information Systems (GIS). Each solution is briefly explained, highlighting its potential applications and benefits in UAS data analysis. To ensure effective implementation, we also present an overview of common best practices in UAS data analysis. These practices encompass data quality assessment, standardization of processing workflows, validation techniques, and result interpretation. By adopting these best practices, organizations can optimize their UAS data analysis processes and achieve more reliable outcomes.

The next part addresses UAS data sharing, providing a general introduction and describing the associated process. The importance of data sharing for collaboration, research, and decision-making is emphasised. An overview of state-of-the-art technological solutions for UAS data sharing are then discussed. This includes cloud-based platforms, data repositories, and specialised data sharing tools. We highlight their key features, security considerations, and interoperability capabilities to facilitate efficient sharing of UAS data among stakeholders. In addition, we outline common best practices for UAS data sharing, such as data formatting standards, metadata inclusion, data access controls, and data privacy considerations. These practices help ensure the secure and effective exchange of UAS data while maintaining data integrity and confidentiality.

A shortlist of available software packages for UAS data analysis and sharing is provided, highlighting their key feature and functionalities. Finally, based on the assessment conducted, we offer future recommendations for UAS data analysis and sharing. These recommendations include exploring emerging technologies, enhancing automation capabilities, integrating real-time analytics, and promoting data standardization. The implementation of these recommendations will further advance the efficiency and effectiveness of UAS data analysis and sharing practices.

In summary, this deliverable provides a comprehensive overview of currently used technical solutions for UAS data analysis and sharing. By leveraging state-of-the-art techniques and adopting best practices, organisations can unlock the full potential of UAS data and derive valuable insights for their respective domains. The recommendations outlined herein aim to drive continuous improvement and innovation in UAS data analysis and sharing.