A data integration methodology for combining visual representations and operations data for applications in forestry

The integration of computer vision in forestry operations is limited by the scarcity of annotated datasets that capture the dynamic and unstructured nature of forest environments. This study addresses this challenge by proposing a semi-automated methodology to synchronize visual representations with machine-generated operations data, enabling efficient dataset creation for machine learning applications. The approach leverages timestamp alignment to automatically extract video frames corresponding to tree-cutting events, significantly reducing manual annotation efforts. In a case study, video data were linked with harvester production records in the StanForD format. Eight video frames were automatically extracted per cutting event, which resulted in the successful capture of all harvested trees. The applied extraction protocol resulted in 88% storage savings and a 98.9% reduction in video frame volume (840 of 75,300 frames), out of which 71% of these frames contained at least a standing tree or one or more logs that were suitable for manual annotation. Operational data were integrated with bounding box annotations, yielding a structured dataset including attributes rarely present in similar datasets (e.g. tree species, dimensions, and quality metrics). The methodology reduced processing time by 92% compared to fully manual methods. By automating data synchronization and extraction, this study lowers the barrier for generating diverse, context-rich datasets critical for advancing computer vision in forestry. The results underscore the potential to integrate real-time environmental representation data with existing operational workflows, highlighting its relevance in supporting precision forestry and autonomous systems.