Topological information extraction from buildings in CityGML

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Ujang

et al. 2020

Abstract. The current trends of 3D scanning technologies allow us to acquire accurate 3D data of large-scale environment efficiently. The 3D data of large-scale environments is essential when generating 3D model is for the visualization of smart cities. For the seamless visualization of 3D model, large data size will be used during the 3D data acquisition. However, the processing time for large data size is time consuming and requires suitable hardware specification. In this study, different hardware capability in processing large data of 3D point cloud for mesh generation is investigated. Light Detection and Ranging (LiDAR) Airborne and Mobile Mapping System (MMS) are used as data input and processed using Bentley ContextCapture software. The study is conducted in Malaysia, specifically in Wilayah Persekutuan Kuala Lumpur and Selangor with the size of 49 km2. Several analyses have been performed to analyse the software and hardware specification based on the 3D mesh model generated. From the finding, we have suggested the most suitable hardware specification for 3D mesh model generation.

Section: D Mesh Modelmentioning

confidence: 69%

Exploratory Study of 3d Point Cloud Triangulation for Smart City Modelling and Visualization

Ariff

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Ujang

et al. 2020

“…In addition, based on this information, responsible agency or authorities can plan and make decisions to solve the pollutants problem on a smaller scale. Furthermore, advanced 3D spatial analysis could be done as suggested by other researchers (Yang et al, 2020;Azri et al, 2016;Salleh et al, 2018;Keling et al 2017)…”

Section: Recommendationmentioning

confidence: 99%

Visualising Urban Air Quality Using Aermod, Calpuff and CFD Models: A Critical Review

Ridzuan

Ujang

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

et al. 2020

Abstract. Degradation of air quality level can affect human’s health especially respiratory and circulatory system. This is because the harmful particles will penetrate into human’s body through exposure to surrounding. The existence of air pollution event is one of the causes for air quality to be low in affected urban area. To monitor this event, a proper management of urban air quality is required to solve and reduce the impact on human and environment. One of the ways to manage urban air quality is by modelling ambient air pollutants. So, this paper reviews three modelling tools which are AERMOD, CALPUFF and CFD in order to visualise the air pollutants in urban area. These three tools have its own capability in modelling the air quality. AERMOD is better to be used in short range dispersion model while CALPUFF is for wide range of dispersion model. Somehow, it is different for CFD model as this model can be used in wide range of application such as air ventilation in clothing and not specifically for air quality modelling only. Because of this, AERMOD and CALPUFF model can be classified in air quality modelling tools group whereas CFD modelling tool is classified into different group namely a non-specific modelling tool group which can be implemented in many fields of study. Earlier air quality researches produced results in two-dimensional (2D) visualization. But there are several of disadvantages for this technique. It cannot provide height information and exact location of pollutants in three-dimensional (3D) as perceived in real world. Moreover, it cannot show a good representation of wind movement throughout the study area. To overcome this problem, the 3D visualization needs to be implemented in the urban air quality study. Thus, this paper intended to give a better understanding on modeling tools with the visualization technique used for the result of performed research.

“…The implementation of 3D data in Geographical Information System (GIS) can be seen in many (Salleh et al, 2018;Keling et al, 2017;Yusoff et al, 2011). Furthermore, the use of GIS in decision making do ease the process in making final verdict (Mohd et al, 2016).…”

Section: Overview On Asset Managementmentioning

confidence: 99%

3d Geometric Extraction Using Segmentation for Asset Management

Anuar

Nasir

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

et al. 2020

Abstract. In recent years, there has been an increase in development of urbanization in the world. Nowadays, all communities in the world are concerned about current technological developments especially in terms of development and management that can facilitate their daily life. In urbanization, smart city is one of modernization changes that improves the infrastructure management, convenience and efficient for the life of citizens. Moreover, 3D asset management is one of the approach of smart city development. Asset management using the 3D concept has been witnessing a welcoming approach due to its high efficiency in organising multiple assets. 3D geometric extraction offers a perfect aid in recording information of an asset such as buildings. The model is derived from the reality techniques where the exterior surfaces of an object are captured in high resolution through the means of special equipment such as airborne imagery. From here, point clouds are generated where the sets of points based on the external surfaces of an object are present. Pre-processing of point clouds should be done in order to perform the 3D modelling. In dealing with point clouds, segmentations are used to investigate the structure of the object with information regarding to different level of sections. The boon behind this segmentation process is to identify different features that is available for the object. In this research, the aim is to analyse the different methodology and algorithm available to segment the point cloud data. Comparison between the results will be made to identify the advantages and disadvantages of the results for the use of asset management.