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.
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