Potential Use of Drone Ultra-High-Definition Videos for Detailed 3D City Modeling

Ongoing developments in video resolution either using consumer-grade or professional cameras has opened opportunities for different applications such as in sports events broadcasting and digital cinematography. In the field of geoinformation science and photogrammetry, image-based 3D city modeling is expected to benefit from this technology development. Highly detailed 3D point clouds with low noise are expected to be produced when using ultra high definition UHD videos (e.g., 4K, 8K). Furthermore, a greater benefit is expected when the UHD videos are captured from the air by consumer-grade or professional drones. To the best of our knowledge, no studies have been published to quantify the expected outputs when using UHD cameras in terms of 3D modeling and point cloud density. In this paper, a quantification is shown about the expected point clouds and orthophotos qualities when using UHD videos from consumer-grade drones and a review of which applications they can be applied in. The results show that an improvement in 3D models of ≅65% relative accuracy and ≅90% in point density can be attained when using 8K video frames compared with HD video frames which will open a wide range of applications and business cases in the near future.

Creation and Spatial Analysis of 3D City Modeling based on GIS Data

The 3D city model is one of the crucial topics that are still under analysis by many engineers and programmers because of the great advancements in data acquisition technologies and 3D computer graphics programming. It is one of the best visualization methods for representing reality. This paper presents different techniques for the creation and spatial analysis of 3D city modeling based on Geographical Information System (GIS) technology using free data sources. To achieve that goal, the Mansoura University campus, located in Mansoura city, Egypt, was chosen as a case study. The minimum data requirements to generate a 3D city model are the terrain, 2D spatial features such as buildings, landscape area and street networks. Moreover, building height is an important attribute in the 3D extrusion process. The main challenge during the creation process is the dearth of accurate free datasets, and the time-consuming editing. Therefore, different data sources are used in this study to evaluate their accuracy and find suitable applications which can use the generated 3D model. Meanwhile, an accurate data source obtained using the traditional survey methods is used for the validation purpose. First, the terrain was obtained from a digital elevation model (DEM) and compared with grid leveling measurements. Second, 2D data were obtained from: the manual digitization from (30 cm) high-resolution imagery, and deep learning structure algorithms to detect the 2D features automatically using an object instance segmentation model and compared the results with the total station survey observations. Different techniques are used to investigate and evaluate the accuracy of these data sources. The procedural modeling technique is applied to generate the 3D city model. TensorFlow & Keras frameworks (Python APIs) were used in this paper; moreover, global mapper, ArcGIS Pro, QGIS and CityEngine software were used. The precision metrics from the trained deep learning model were 0.78 for buildings, 0.62 for streets and 0.89 for landscape areas. Despite, the manual digitizing results are better than the results from deep learning, but the extracted features accuracy is accepted and can be used in the creation process in the cases not require a highly accurate 3D model. The flood impact scenario is simulated as an application of spatial analysis on the generated 3D city model. Doi: 10.28991/CEJ-2022-08-01-08 Full Text: PDF

A FIRST ATTEMPT TO DEFINE LEVEL OF DETAILS BASED ON DECISION-MAKING TASKS: APPLICATION TO UNDERGROUND UTILITY NETWORK

Level of detail (LoD) is a key concept for 3D city modeling to optimise visualisation. The LoDs of CityGML shows this trend. This paper explores the relevance of having LoD for visualising 3D model of Underground Utility Networks (UUN). A new approach is proposed for designing multiple LoDs modeling in creating an explicit link between the content of the 3D model and the decision-making process (or operational tasks) to be performed by a user. This Multiple Level of detail Approach (MLA) is divided into four steps. The first step requires defining the visualisation needs in terms of five variables (geometry, topology, semantic, contextual information, and semiology). Next, tasks to be performed are analyzed and categorized. Finally, a matrix of possible LoDs is created for all tasks and the minimum LoD required is proposed. In this paper, we applied this approach for the use case of granting connection permits to water and sewer networks. Learning aspects are proposed in the discussion.

Semantic 3D City Modeling and BIM

Semantic 3D city modeling and building information modeling (BIM) are methods for modeling, creating, and analyzing three-dimensional representations of physical objects of the environment. Digital modeling of the built environment has been approached from at least four different domains: computer graphics and gaming, planning and construction, urban simulation, and geomatics. This chapter introduces the similarities and differences of 3D models from these disciplines with regard to aspects like scale, level of detail, representation of spatial and semantic characteristics, and appearance. Exemplified by the international standards CityGML and Industry Foundation Classes (IFC), information models from semantic 3D city modeling and BIM and their corresponding modeling approaches are explored, and the relationships between them are discussed. Based on use cases from infrastructure planning, approaches for integrating information from semantic 3D city modeling and BIM, such as semantic transformation between CityGML and IFC, are described. Furthermore, the role of semantic 3D city modeling and BIM for recent developments in urban informatics, such as smart cities and digital twins, is investigated and illustrated by real-world examples.

Using Climate-Sensitive 3D City Modeling to Analyze Outdoor Thermal Comfort in Urban Areas

With increasing urbanization, climate change poses an unprecedented threat, and climate-sensitive urban management is highly demanded. Mitigating climate change undoubtedly requires smarter urban design tools and techniques than ever before. With the continuous evolution of geospatial technologies and an added benefit of analyzing and virtually visualizing our world in three dimensions, the focus is now shifting from a traditional 2D to a more complicated 3D spatial design and assessment with increasing potential of supporting climate-responsive urban decisions. This paper focuses on using 3D city models to calculate the mean radiant temperature (Tmrt) as an outdoor thermal comfort indicator in terms of assessing the spatiotemporal distribution of heat stress on the district scale. The analysis is done to evaluate planning scenarios for a district transformation in Montreal/Canada. The research identifies a systematic workflow to assess and upgrade the outdoor thermal comfort using the contribution of ArcGIS CityEngine for 3D city modeling and the open-source model of solar longwave environmental irradiance geometry (SOLWEIG) as the climate assessment model. A statistically downscaled weather profile for the warmest year predicted before 2050 (2047) is used for climate data. The outcome shows the workflow capacity for the structured recognition of area under heat stress alongside supporting the efficient intervention, the tree placement as a passive strategy of heat mitigation. The adaptability of workflow with the various urban scale makes it an effective response to the technical challenges of urban designers for decision-making and action planning. However, the discovered technical issues in data conversion and wall surface albedo processing call for the climate assessment model improvement as future demand.