Automated construction progress monitoring using as-planned building information modeling (BIM) and as-built point cloud data integration has substantial potential and could lead to the fast-tracking of construction work and identifying discrepancies. Laser scanning is becoming mainstream for conducting construction surveys due to the accuracy of the data obtained and the speed of the process; however, construction progress monitoring techniques are still limited because of the complexity of the methods, incompleteness of the scanned areas, or the obstructions by temporary objects in construction sites. The novel method proposed within this study enables the extracting of BIM data, calculating the plane equation of the faces, and performing a point-to-plane distance estimation, which successfully overcomes some limitations reported in previous studies, including automated object detection in an occluded environment. Six datasets consisting of point clouds collected by static and mobile laser scanning techniques including the corresponding BIM models were analyzed. In all the analyzed cases, the proposed method automatically detected whether the construction of an object was completed or not in the as-built point cloud compared to the provided as-planned BIM model.

Digital Twins rely on accurate geometric models to ensure reliable representation, yet maintaining and updating these models remains a persistent challenge. This paper addresses one aspect of this challenge by focusing on the verification of photogrammetry-based models. It introduces a verification framework that defines measurable data quality elements and establishes conditions to assess whether model quality is maintained, improved, or degraded. Validation through a university building case study demonstrates the framework’s ability to detect quality differences between visually similar models. Meeting only one of the three verification conditions, the new model shows quality degradation, primarily due to reduced positional accuracy and resolution, making it unsuitable to replace the previous version used in the Digital Twin. Additionally, the developed web tool prototype enables the automated calculation of the framework’s verification scores. This study contributes to the growing discussion on Digital Twin maintenance by providing practical insights for improving the reliability of geometric models.

CE ( Citizen engagement) is an activity that aims to provide a way to link citizens and organizations. As such it has a bidirectional channel of communication, from citizens to organization and from organization to citizens. These channels of communication should be user friendly, easily accessible and also need to fulfil other requisites depending on the use case. In that sense, CE still poses a research challenge to provide a complete solution. To this end, Digital Twin (DT) technology has been used to develop tools that integrate and visualize data of different types like 2D/3D maps digital models, sensor readings and any multimedia data. DTs have the capacity to provide as well user-friendly and easily accessible interfaces. This work focuses on the application of DT technology to DCU (Dublin City University), specifically the DCU C&C (Care & Connect) initiative that was charged with raising awareness of the services, facilities, and increasing feeling of belonging and inclusion across the DCU campuses. Services such as sexual health services, wellness support, and facilities like quiet rooms, sensory pods, and room locations. The use of DT technology has enabled the development of a live Campus Explorer with which students can interact in a user-friendly and easily accessible manner on a web-based interface using mobile phones, tablets or laptops. This paper provides an overview of the DCU Campus Explorer and how it is being used in practice by 22,000 students, more than 3,000 staff plus visitors at https://www.dcu.ie/CampusExplorer .

Moving object path prediction in traffic scenes from the perspective of a moving vehicle can improve safety on the road, which is the aim of Advanced Driver Assistance Systems (ADAS). However, this task still remains a challenge. Work has been carried out on the use of $x,y$ positional information of the moving objects only. However, besides positional information there is more information that surrounds a vehicle that can be leveraged in the prediction along with the $x,y$ features. This is known as contextual information. In this work, a deep exploration of these features is carried out by evaluating different types of data, using different fusion strategies. The core architectures of this model are CNN and LSTM architectures. It is concluded that in the prediction task, not only are the features important, but the way they are fused in the developed architecture is also of importance.