OpenConstruction: A Systematic Synthesis of Open Visual Data Sets for Data-Centric Intelligence in Construction Monitoring

The construction industry increasingly relies on visual data to support artificial intelligence and machine learning applications for site monitoring. High-quality, domain-specific data sets, comprising images, videos, and point clouds capture site geometry and spatiotemporal dynamics, including the location and interaction of objects, workers, and materials. However, despite growing interest in leveraging these visual data sets, existing resources vary widely in size, data modalities, annotation quality, and representativeness of real-world construction conditions. A systematic review to categorize their data characteristics and application contexts is still lacking, limiting the community's ability to fully understand the data set landscape, identify critical gaps, and guide future directions toward more effective, reliable, and scalable AI applications in construction. To address this gap, this review conducts an extensive search of academic databases and open-data platforms, yielding 51 publicly available visual data sets that span the 2005-2024 period. These data sets are categorized using a structured data schema covering (1) data fundamentals (e.g., size and license), (2) data modalities (e.g., point cloud, thermal), (3) annotation frameworks (e.g., bounding boxes, keypoints), and (4) downstream application domains (e.g., safety, progress tracking). This study synthesizes these findings into an open-source catalog, OpenConstruction, supporting data-driven method development. Further, the study discusses several critical limitations in the existing construction data set landscape and presents a roadmap for future data infrastructure anchored in the findability, accessibility, interoperability, and reusability and domain-specific principles. By reviewing the current landscape and outlining strategic priorities, this study supports the advancement of datacentric solutions in the construction sector.