A 3D INDOOR-OUTDOOR BENCHMARK DATASET FOR LoD3 BUILDING POINT CLOUD SEMANTIC SEGMENTATION

Deep learning (DL) algorithms require high quality training samples as well as accurate and thorough annotations to work effectively. Up until now a limited number of datasets are available to train DL techniques for semantic segmentation of 3D building point clouds, except a few ones focusing on specific categories of constructions (e.g., cultural heritage buildings). This paper presents a new 3D Indoor/Outdoor building dataset (BIO dataset), which is aimed to provide a highly accurate, detailed, and comprehensive dataset to be used for applications related to sematic classification of buildings based on point clouds and meshes. This benchmark dataset contains 100 building models generated from existing polygonal models and belonging to different categories. These include commercial buildings, residential houses, industrial and institutional buildings. Structural elements of buildings are annotated into 11 semantic categories, following standards from IFC and CityGML. To verify the applicability of the BIO dataset for the semantic segmentation task, it has been successfully tested by using one machine learning technique and four different DL algorithms

MULTILEVEL SEMANTIC MODELLING OF URBAN BUILDING SPACE BASED ON THE GEOMETRIC CHARACTERISTICS IN 3D ENVIRONMENT

Data model is the basis of all the functions of geographic information system. As the land use structure has become more and more complicated in cities, the traditional geometric model are not able to satisfy the increasing demands of precise urban form recognition and space management. Against the shortcomings, we propose to construct a multilevel semantic model for better description of the spatial composition of each building and the relationships among different buildings. Based on the 3D surface models constructed with photogrammetry and remote sensing methods, the semantic model is generated to depict the urban building space hierarchically, from stories, buildings, subareas to the entire city zone. On the one hand, to figure out the stories of each building, the geometric 3D model is segmented vertically with reference to the compositional structures and spatial distributions of the functional features on the surfaces. On the other hand, to determine the subareas of the city, the buildings are grouped into meaningful clusters according to their geometric shape characteristics. Experiments were conducted on a small district with both commercial and residential buildings, and the effectiveness of the proposed approach and usage of the semantic model were demonstrated

A dynamic approach for evacuees’ distribution and optimal routing in hazardous environments

© 2018 Elsevier B.V. In a complex built environment, the situation changes rapidly during an emergency event. Typically, available systems rely heavily on a static scenario in the calculation of safest routes for evacuation. In addition, egress route calculation and evacuation simulations are performed separately from path-finding for rescue teams. In this paper, we propose a state-of-the-art dynamic approach, which deals not only with a 3D environment, shape of spaces and hazard locations, but also with the dynamic distribution of occupants during evacuation. A database of densities and information about hazard influence are generated and used to calculate optimal paths for rescue teams. Three simulation scenarios were rigorously compared in this study, namely static with constant density values determined for subsequent stages of evacuation, semi-dynamic with densities representing an actual people distribution in a building during evacuation simulation, and dynamic with temporal distribution of evacuees stored in a database, and dynamically used in optimal path calculations. The findings revealed that static simulation is significantly different from semi-dynamic and dynamic simulations, and each type of simulation is better suited for the decision task at hand. These results have significant implications on achieving a rapid and safe evacuation of people during an emergency event

Digital Asset in the System of Real Estate Management

This study examines the definition of a digital asset and considers its properties in the system of managing real estate that helps to reduce transaction costs. The intermediate results of the development of a service for digital asset management are presented. In practice, a method of accounting and using data about an object with the use of information modeling technologies is shown. The structure of the code from the point of view of programming is given. The proposed method is aimed at recording data throughout the entire life cycle of a capital construction facility with an emphasis on the operation & maintenance stage, as at the life cycle stage, the least susceptible to the introduction of information modeling technologies. The current research areas in the field of «digital asset» management and digital «asset management» are identified

Developing Buildings Permits Systems Platforms (BPSP) for driving change to introduce GeoBIM potentials, challenges, and opportunities

Cities growth requires development in tools and applications to manage achieving city strategic vision with clear smart policies, which is challenging to achieve with traditional methods. So, the importance of adopting integrated technologies like BIM, GIS, and GeoBIM is becoming essential and beneficial. Many countries have developed using such technologies to enhance the performance of Building Permits Systems (BPS). The need to build a system that unifies practices, standards, and protocols within one place in a manageable platform shall enhance the performance of BPS. So, the aim is to assess the capabilities of implementing GeoBIM in Saudi BPSP municipalities for developing procedures and workflows and to define the potentials and barriers of change in systems. So, the research focused on developing existing systems with a semi-structured interview evaluates the capabilities and workflows for adopting GeoBIM in the Riyadh, Jeddah, and Mecca municipalities.  In conclusion, the research results 21 factors of GeoBIM implementation that shall initiate a foundation for further studies fulfilling gaps in such study areas

Critical analysis for big data studies in construction: significant gaps in knowledge

Purpose The purpose of this paper is to identify the gaps and potential future research avenues in the big data research specifically in the construction industry. Design/methodology/approach The paper adopts systematic literature review (SLR) approach to observe and understand trends and extant patterns/themes in the big data analytics (BDA) research area particularly in construction-specific literature. Findings A significant rise in construction big data research is identified with an increasing trend in number of yearly articles. The main themes discussed were big data as a concept, big data analytical methods/techniques, big data opportunities – challenges and big data application. The paper emphasises “the implication of big data in to overall sustainability” as a gap that needs to be addressed. These implications are categorised as social, economic and environmental aspects. Research limitations/implications The SLR is carried out for construction technology and management research for the time period of 2007–2017 in Scopus and emerald databases only. Practical implications The paper enables practitioners to explore the key themes discussed around big data research as well as the practical applicability of big data techniques. The advances in existing big data research inform practitioners the current social, economic and environmental implications of big data which would ultimately help them to incorporate into their strategies to pursue competitive advantage. Identification of knowledge gaps helps keep the academic research move forward for a continuously evolving body of knowledge. The suggested new research avenues will inform future researchers for potential trending and untouched areas for research. Social implications Identification of knowledge gaps helps keep the academic research move forward for continuous improvement while learning. The continuously evolving body of knowledge is an asset to the society in terms of revealing the truth about emerging technologies. Originality/value There is currently no comprehensive review that addresses social, economic and environmental implications of big data in construction literature. Through this paper, these gaps are identified and filled in an understandable way. This paper establishes these gaps as key issues to consider for the continuous future improvement of big data research in the context of the construction industry