Demystifying the Potential of ChatGPT-4 Vision for Construction Progress Monitoring

TL;DR

This paper investigates GPT-4 Vision's ability to analyze aerial images for construction progress monitoring, highlighting its strengths in identifying construction elements and discussing future improvements for industry application.

Contribution

It demonstrates GPT-4 Vision's capabilities in construction scene analysis and progress tracking, and explores future enhancements for better industry integration.

Findings

Proficient in identifying construction stages, materials, and machinery.

Challenges with precise object localization and segmentation.

Potential for future advancements with domain-specific training.

Abstract

The integration of Large Vision-Language Models (LVLMs) such as OpenAI's GPT-4 Vision into various sectors has marked a significant evolution in the field of artificial intelligence, particularly in the analysis and interpretation of visual data. This paper explores the practical application of GPT-4 Vision in the construction industry, focusing on its capabilities in monitoring and tracking the progress of construction projects. Utilizing high-resolution aerial imagery of construction sites, the study examines how GPT-4 Vision performs detailed scene analysis and tracks developmental changes over time. The findings demonstrate that while GPT-4 Vision is proficient in identifying construction stages, materials, and machinery, it faces challenges with precise object localization and segmentation. Despite these limitations, the potential for future advancements in this technology is considerable. This research not only highlights the current state and opportunities of using LVLMs in construction but also discusses future directions for enhancing the model's utility through domain-specific training and integration with other computer vision techniques and digital twins.