This paper presents a comprehensive overview of the rapid damage assessment and reconnaissance efforts following the devastating earthquakes on February 6, 2023, in Türkiye. It specifically focuses on implementing the SiteEye Disaster Plugin, an additional component of SiteEye software developed by i4 Company engineers and Middle East Technical University researchers. This tool played a critical role in managing and analyzing a massive dataset comprising over 28,000 images and videos. The research highlights the plugin’s innovative features, such as offline data collection, georeferenced-based layering, and an integrated damage classification system, significantly improving earthquake impact assessments’ accuracy and efficiency. It also underscores the importance of interdisciplinary collaboration involving national and international teams and the role of open data in disaster management. The findings demonstrate how digital technologies can transform the field of disaster response, offering new approaches for rapid assessment and effective management in the aftermath of seismic events. This research contributes valuable insights into enhancing disaster preparedness and response strategies, particularly in earthquake-prone areas.

Applying deep learning algorithms in the construction industry holds tremendous potential for enhancing site management, safety, and efficiency. The development of such algorithms necessitates a comprehensive and diverse image dataset. This study introduces the Aerial Image Dataset for Construction (AIDCON), a novel aerial image collection containing 9563 construction machines across nine categories annotated at the pixel level, carrying critical value for researchers and professionals seeking to develop and refine object detection and segmentation algorithms across various construction projects. The study highlights the benefits of utilizing UAV-captured images by evaluating the performance of five cutting-edge deep learning algorithms—Mask R-CNN, Cascade Mask R-CNN, Mask Scoring R-CNN, Hybrid Task Cascade, and Pointrend—on the AIDCON dataset. It underscores the significance of clustering strategies for generating reliable and robust outcomes. The AIDCON dataset’s unique aerial perspective aids in reducing occlusions and provides comprehensive site overviews, facilitating better object positioning and segmentation. The findings presented in this paper have far-reaching implications for the construction industry, as they enhance construction site efficiency while setting the stage for future advancements in construction site monitoring and management utilizing remote sensing technologies.

Development of a lean and automated progress tracking system is one of the most challenging subjects in the construction industry. Since the current methods depend on individual and manual inspections of the site, they may have various problems. For instance, results include a natural bias since field personnel makes interpretations depending on their educational and expertise level. From the practical point of view, manual data collection and their comparison with construction drawings and schedules are inefficient as it takes too much time. With the recent advancement of Unmanned Aerial Vehicles (UAVs) into the market and inclusion of lean project management principles, project management studies are inclined towards the digital and lean approaches. Within this perspective, the objective of this study is to reveal the use of UAVs as a lean project management tool. This paper indicates merely that UAVs have the potential for such a purpose.

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.