The growing rate of urbanization and vertical urban development has aroused the significance of geo-related variables for property units disposed vertically within the same building. Among these, 3D indoor physical and outdoor environmental variables are impacting the property value for each building unit. However, in the literature, the identified 3D variables, by using hedonic pricing models (HPM) for property valuation, are mainly restricted to 3D visualization. Their use in 3D simulation for an accurate evaluation of the property value is still limited. Furthermore, their value is often defined for a specific valuation purpose (e.g., taxation). This paper aims to investigate 3D variables with a significant impact on property value, to combine them with 3D technical requirements and to be integrated in a future valuation model. Moreover, their 3D spatial and non-spatial elements are analyzed to identify which variables can be provided from 3D city models and building scale elements. To accomplish this, the potential of 3D building information modeling (BIM) and city information modeling (CIM) in property valuation is examined. From indoors; BIM/IFC (Industry Foundation Classes) models are the main data sources for structural and living quality variables. While from outdoors, environmental variables and the surrounding building’s information are provided from 3D city models (CityGML).

Abstract. With the increasing volume of 3D applications using immersive technologies such as virtual, augmented and mixed reality, it is very interesting to create better ways to integrate unstructured 3D data such as point clouds as a source of data. Indeed, this can lead to an efficient workflow from 3D capture to 3D immersive environment creation without the need to derive 3D model, and lengthy optimization pipelines. In this paper, the main focus is on the direct classification and integration of massive 3D point clouds in a virtual reality (VR) environment. The emphasis is put on leveraging open-source frameworks for an easy replication of the findings. First, we develop a semi-automatic segmentation approach to provide semantic descriptors (mainly classes) to groups of points. We then build an octree data structure leveraged through out-of-core algorithms to load in real time and continuously only the points that are in the VR user's field of view. Then, we provide an open-source solution using Unity with a user interface for VR point cloud interaction and visualisation. Finally, we provide a full semantic VR data integration enhanced through developed shaders for future spatio-semantic queries. We tested our approach on several datasets of which a point cloud composed of 2.3 billion points, representing the heritage site of the castle of Jehay (Belgium). The results underline the efficiency and performance of the solution for visualizing classifieds massive point clouds in virtual environments with more than 100 frame per second.

Housing valuation is a process of determining an accurate estimate of the market price of a property. Current methods and tools are mainly based on sales prices comparison with recent transactions, which is the major method applied in the taxation and cadastre services in Morocco. However, housing properties are in general heterogenous and unique in their shapes, construction materials, orientation, location and other environmental factors. These parameters are taken into consideration by the hedonic pricing method. Many of the researches about housing valuation are based on the geographical location as the main spatial factor affecting the property value. 2D GIS (Geographic Information System) applications used in this respect are limited in terms of communicating efficiently the complexity of a 3D building structure and modeling accurately environmental factors. Such factors could only be considered through 3D modeling and building information modeling (BIM). In this paper, we will try, through a brief review, to point out the weaknesses and drawbacks of the conventional valuation methods. Then, we will demonstrate the BIM potential in real valuation as an emerging technology and process used to mainly improve the housing valuation system based on the hedonic approach. Many studies are, nowadays, widely exploring the use of BIM in the building cost estimation, but this is an embryonic area of research in real estate valuation system. Therefore, this paper examines also the first methodological guidelines for an advanced housing valuation approach by implementing a BIM prototype based on the hedonic pricing method.

Abstract. The raw nature of point clouds is an important challenge for their direct exploitation in architecture, engineering and construction applications. Particularly, their lack of semantics hinders their utility for automatic workflows (Poux, 2019). In addition, the volume and the irregularity of the structure of point clouds makes it difficult to directly and automatically classify datasets efficiently, especially when compared to the state-of-the art 2D raster classification. Recently, with the advances in deep learning models such as convolutional neural networks (CNNs) , the performance of image-based classification of remote sensing scenes has improved considerably (Chen et al., 2018; Cheng et al., 2017). In this research, we examine a simple and innovative approach that represent large 3D point clouds through multiple 2D projections to leverage learning approaches based on 2D images. In other words, the approach in this study proposes an automatic process for extracting 360° panoramas, enhancing these to be able to leverage raster data to obtain domain-base semantic enrichment possibilities. Indeed, it is very important to obtain a rigorous characterization for use in the classification of a point cloud. Especially because there is a very large variety of 3D point cloud domain applications. In order to test the adequacy of the method and its potential for generalization, several tests were performed on different datasets. The developed semantic augmentation algorithm uses only the attributes X, Y, Z and camera positions as inputs.

Three-dimensional digital models play a pivotal role in city planning, monitoring, and sustainable management of smart and Digital Twin Cities (DTCs). In this context, semantic segmentation of airborne 3D point clouds is crucial for modeling, simulating, and understanding large-scale urban environments. Previous research studies have demonstrated that the performance of 3D semantic segmentation can be improved by fusing 3D point clouds and other data sources. In this paper, a new prior-level fusion approach is proposed for semantic segmentation of large-scale urban areas using optical images and point clouds. The proposed approach uses image classification obtained by the Maximum Likelihood Classifier as the prior knowledge for 3D semantic segmentation. Afterwards, the raster values from classified images are assigned to Lidar point clouds at the data preparation step. Finally, an advanced Deep Learning model (RandLaNet) is adopted to perform the 3D semantic segmentation. The results show that the proposed approach provides good results in terms of both evaluation metrics and visual examination with a higher Intersection over Union (96%) on the created dataset, compared with (92%) for the non-fusion approach.

Change detection is an important step for the characterization of object dynamics at the earth’s surface. In multi-temporal point clouds, the main challenge is to detect true changes at different granularities in a scene subject to significant noise and occlusion. To better understand new research perspectives in this field, a deep review of recent advances in 3D change detection methods is needed. To this end, we present a comprehensive review of the state of the art of 3D change detection approaches, mainly those using 3D point clouds. We review standard methods and recent advances in the use of machine and deep learning for change detection. In addition, the paper presents a summary of 3D point cloud benchmark datasets from different sensors (aerial, mobile, and static), together with associated information. We also investigate representative evaluation metrics for this task. To finish, we present open questions and research perspectives. By reviewing the relevant papers in the field, we highlight the potential of bi- and multi-temporal point clouds for better monitoring analysis for various applications.

Augmented reality (AR) is a relevant technology, which has demonstrated to be efficient for several applications, especially in the architecture, engineering, construction and operation (AECO) domain, where the integration of building information modeling (BIM) and AR has proved to be optimal in handling construction projects. However, the main challenge when integrating a virtual 3D model in an AR environment is the lack of precision and accuracy of placement that can occur between the real and the virtual environments. Although methods for placement via AR have been reported in the literature, there is a lack of investigations addressing their evaluation. Therefore, this paper proposes a methodology to perform a quantitative and qualitative assessment of several AR placement methods and a discussion about their usability in the specific context of AECO. We adopt root mean square error (RMSE) to quantify the placement accuracy of a 3D model and standard deviation to examine its stability (jittering). The results revealed that the AR placement error range is extremely wide (from a few centimeters up to meters). In marker-based methods, the results showed centimeter-range in both indoor and outdoor environments, compared to other methods (Inertial, Marker-less, etc.), while marker-less methods have widely varying error range from centimeters to a few meters. Other commercial solutions based on placement-sensors (GNSS and IMU), such as Trimble SiteVision, have proven placement performance in manual mode with centimeter order, while for the automatic mode, the order of placement and stability is metric, due to the low coverage of RTX (real time extended) in the study area.

With rapid population growth, there is an increasing demand for vertical use of space. The wide spread of complex and high-rise buildings, as well as the increasing number of infrastructure above or underground, requires new methods for efficient management of land property. 3D cadastre has, thus, become a necessity for land administration. However, the success of 3D cadastral systems relies on the definition of legal and institutional frameworks and requires implementing performant technical solutions. The potential of BIM and 3D GIS in this field has been demonstrated by several authors. However, cadastral development is strongly related to the national context of each country in terms of laws, institutions, etc. In this paper, an integrated approach based on BIM and 3D GIS for the implementation of a 3D cadastre in Morocco is presented. This approach demonstrates the relevance of such integration for the efficient management of cadastral information. First, a Conceptual Data Model (CDM) based on an extension of CityGML, was proposed for the management of cadastral information in Morocco. Then, a BIM modeling process was developed according to the model’s specifications and then translated to CityGML format. After that, a 3D Geodatabase was implemented in ArcGIS based on the proposed CDM. Our method was applied to a case of co-ownership building, showing several difficulties and limits in terms of 2D representation. The results show several advantages in terms of representation and management of 3D cadastral objects. In addition, some improvements are proposed to extend the concept of co-owner share to a volumetric calculation.