Bi-Directional Image-to-Text Mapping for NLP-Based Schedule Generation and Computer Vision Progress Monitoring

State-of-the-art in construction document analytics and progress detection has experienced accelerated growth over the last decade. However, each area encountered isolated growth, not considering their interactions. Today, progress monitoring practices are often neglected due to requiring manual input of visible progress against schedules. Such a challenge can be attributed to (1) vision-based progress tracking lacking formal construction work templates applied in common construction workflows, and (2) research in automated schedule generation and analytics lacking focus on extracting fragnets from a body of existing schedules. This study brings together insights on research trends for automated schedule generation and analytics using Natural Language Processing (NLP) and detection of under-construction objects using Computer Vision. Finally, the AIConstruct system is presented to demonstrate, for the first time, how the integration of text and image can create seamless data synchronization for construction progress monitoring and automated schedule generation, unlocking a new research paradigm.