In the construction industry, occupational health and safety plays a critical role in preventing occupational accidents and increasing productivity. In recent years, computer vision and artificial intelligence-based systems have made significant contributions to improving these processes through automatic detection and tracking of objects. The aim of this study was to fine-tune object detection models and integrate them with Large Language Models for (i). accurate detection of personal protective equipment (PPE) by specifically focusing on helmets and (ii). providing real-time recommendations based on the detections for supporting the use of helmets in construction sites. For achieving the first objective of the study, large YOLOv8/v11/v12 models were trained using a helmet dataset consisting of 16,867 images. The dataset was divided into two classes: “Head (No Helmet)” and “Helmet”. The model, once trained, was able to analyze an image from a construction site and detect and count the people with and without helmets. A tool with the aim of providing advice to workers in real time was developed to fulfil the second objective of the study. The developed tool provides the counts of the people based on video feeds or analyzing a series of images and provides recommendations on occupational safety (based on the detections from the video feed and images) through an OpenAI GPT-3.5-turbo Large Language Model and with a Streamlit-based GUI. The use of YOLO enables quick and accurate detections; in addition, the use of the OpenAI model API serves the exact same purpose. The combination of the YOLO model and OpenAI model API enables near-real-time responses to the user over the web. The paper elaborates on the fine tuning of the detection model with the helmet dataset and the development of the real-time advisory tool.

This study proposes a sequential AI workflow for generating LOD3-oriented digital building representations from a single architectural photograph. The novelty of the approach lies in the use of Gemini Flash 2.5 for preprocessing and Hunyuan3D 2.1 for image-to-3D generation within a preprocessing–reconstruction decoupled pipeline. Rather than directly producing a 3D model from an uncontrolled photograph, the workflow first normalizes the image by isolating the building, converting the scene to daylight, and approximating an isometric view, and then converts this image into a textured 3D mesh. In this way, the method offers a rapid, low-data, and reproducible alternative for cultural heritage visualization, comparative analysis, and digital archiving. Experiments were conducted on 16 photographs of architectural structures representing typologies from the early Republican and Ottoman periods. The findings demonstrate that the method offers a tool for the digital documentation of cultural heritage, archiving and academic research. The resulting 3D outputs are suitable for urban silhouette analysis at the LOD3, volumetric comparison, testing urban design scenarios, and the creation of digital heritage archives. The method works with a single photograph and cloud-based AI models, which is advantageous when on-site scanning is difficult, risky, or costly. Furthermore, the workflow can be enhanced with multiple images, text input, and other sensor data to produce digital twins with geometric accuracy. In this respect, the study demonstrates that by combining cutting-edge technologies in 3D modeling from a single image, 3D twins of architectural objects can be produced, and proposes an AI-powered method for creating digital architectural representations.

This paper reviews the status and progress of Building Information Modelling (BIM) with geoinformation systems in Malaysia and Turkiye, emphasizing their respective efforts toward 3D digital land administration. Both countries recognize BIM as a key enabler in the digital transformation of the built environment, supporting spatial data management, cadastral modernization, and smart urban governance. In Malaysia, particularly in Sarawak, the integration of BIM with the Land Administration Domain Model (LADM) focuses on native customary rights (NCR) land and enhancing strata management through semantic enrichment of 3D building data. This research-driven initiative by Universiti Teknologi Malaysia (UTM) and the Land and Survey Department Sarawak highlights the potential of BIM – LADM mapping for transparent and efficient 3D registration of legal spaces. Conversely, Turkiye has made notable progress through government-led pilot projects such as Amasya and Yenimahalle, which demonstrate operational integration of BIM, LADM, and CityGML for 3D cadastre, condominium ownership, and urban infrastructure management. In comparison, Malaysia takes approaches driven by local legal needs, while Turkiye focused on national policies and data standards. Despite these differing approaches, both nations face shared challenges in semantic conversion, georeferencing, data harmonization, and limited institutional capacity. The findings conclude that BIM and geoinformation offers significant potential for improving digital land administration in both countries. To advance this goal, the paper recommends strengthening policy frameworks, enhancing capacity building, and promoting collaborative research to develop standardized workflows for BIM – LADM integration. Joint Malaysia – Turkiye initiatives under international platforms such as ISPRS or FIG could accelerate the realization of interoperable 3D cadastral systems and contribute to global advancements in smart land governance.