Large-scale 3D City Models Research Articles

DEVELOPMENT OF A DIGITAL 3D PARTICIPATION PLATFORM – CASE STUDY OF WEILIMDORF (STUTTGART, GERMANY)

Abstract. This paper explains the development of a 3D city model-based Public Participation Platform as a prototype and its implementation in a real-world public participation process to redevelop the Weilimdorf area of Stuttgart city. Alongside conducting Weilimdorf’s public participation process, the goal of the mentioned public participation platform is to research citizens’ acceptance of such tools. The usage of digital tools has become more critical for participation processes. The need for social distancing expedites this change, particularly during the pandemic. Previous research frequently focuses on 2D platforms and smaller sample sizes but nevertheless shows the importance of such tools. However, with current developments in geospatial and web streaming technologies, it has become easier and faster to visualize large-scale 3D city models over the web. In this research, these technologies were used by the citizens of the Weilimdorf area to evaluate the usability of the platform and collect their feedback. The result shows that such a digital public participation platform is a valuable supplement to traditional in-person public participation methods.

AUTOMATIC 3D BUILDINGS COMPACT RECONSTRUCTION FROM LIDAR POINT CLOUDS

Abstract. Point clouds generated from aerial LiDAR and photogrammetric techniques are great ways to obtain valuable spatial insights over large scale. However, their nature hinders the direct extraction and sharing of underlying information. The generation of consistent large-scale 3D city models from this real-world data is a major challenge. Specifically, the integration in workflows usable by decision-making scenarios demands that the data is structured, rich and exchangeable. CityGML permits new advances in terms of interoperable endeavour to use city models in a collaborative way. Efforts have led to render good-looking digital twins of cities but few of them take into account their potential use in finite elements simulations (wind, floods, heat radiation model, etc.). In this paper, we target the automatic reconstruction of consistent 3D city buildings highlighting closed solids, coherent surface junctions, perfect snapping of vertices, etc. It specifically investigates the topological and geometrical consistency of generated models from aerial LiDAR point cloud, formatted following the CityJSON specifications. These models are then usable to store relevant information and provides geometries usable within complex computations such as computational fluid dynamics, free of local inconsistencies (e.g. holes and unclosed solids).

Building Virtual 3D City Model for Smart Cities Applications: A Case Study on Campus Area of the University of Novi Sad

The Smart Cities data and applications need to replicate, as faithfully as possible, the state of the city and to simulate possible alternative futures. In order to do this, the modelling of the city should cover all aspects of the city that are relevant to the problems that require smart solutions. In this context, 2D and 3D spatial data play a key role, in particular 3D city models. One of the methods for collecting data that can be used for developing such 3D city models is Light Detection and Ranging (LiDAR), a technology that has provided opportunities to generate large-scale 3D city models at relatively low cost. The collected data is further processed to obtain fully developed photorealistic virtual 3D city models. The goal of this research is to develop virtual 3D city model based on airborne LiDAR surveying and to analyze its applicability toward Smart Cities applications. It this paper, we present workflow that goes from data collection by LiDAR, through extract, transform, load (ETL) transformations and data processing to developing 3D virtual city model and finally discuss its future potential usage scenarios in various fields of application such as modern ICT-based urban planning and 3D cadaster. The results are presented on the case study of campus area of the University of Novi Sad.

Delivering time-evolving 3D city models for web visualization

ABSTRACT Studying and planning urban evolution is essential for understanding the past and designing the cities of the future and can be facilitated by providing means for sharing, visualizing, and navigating in cities, on the web, in space and in time. Standard formats, methods, and tools exist for visualizing large-scale 3D cities on the web. In this paper, we go further by integrating the temporal dimension of cities in geospatial web delivery standard formats. In doing so, we enable interactive visualization of large-scale time-evolving 3D city models on the web. A key characteristic of this paper lies in the proposed four-step generic approach. First, we design a generic conceptual model of standard formats for delivering 3D cities on the web. Then, we formalize and integrate the temporal dimension of cities into this generic conceptual model. Following which, we specify the conceptual model in the 3D Tiles standard at logical and technical specification levels, resulting in an extension of 3D Tiles for delivering time-evolving 3D city models on the web. Finally, we propose an open-source implementation, experiments, and an evaluation of the propositions and visualization rules. We also provide access to reproducibility notes allowing researchers to replicate all the experiments.

Creating 3D city models with building footprints and LIDAR point cloud classification: A machine learning approach

The increasing availability of open geospatial data, such as building footprint vector data and LiDAR (Light Detection and Ranging) point clouds, has provided opportunities to generate large-scale 3D city models at low cost. However, using unclassified point clouds with building footprints to estimate building heights may yield erroneous results due to potential errors and anomalies in both datasets and their integration. Some of the points within footprints often reflect irrelevant objects other than roofs, leading to biases in height estimation, and few studies have developed systematic methods to filter them out. In this paper, a LiDAR point classification methodology is proposed that extracts only rooftop points for building height estimation. The LiDAR points are characterized by point, footprint, and neighborhood-based features and classified by the Random Forest (RF) algorithm into four classes – rooftop, wall, ground, and high outlier. The percentage of correctly classified points among 15,577 sample points in Columbus, Ohio, amounts to 96.5%. Conducting this classification separately for different building types (commercial, residential, skyscraper, and small constructions) does not significantly change the overall accuracy. The footprint-based features contribute most to predicting the classes correctly. Height validation results based on a sample of 498 buildings show that (1) using average or median heights with classified points provides the best estimates, minimizing the disparities between computed heights and ground truth and (2) the RF method yields outcomes much closer to ground truth than earlier classification approaches. Some outcomes are visualized in 3D format using Google Earth 3D Imagery and ArcScene.

A customizable framework for computing sky view factor from large-scale 3D city models

A common approach to derive the sky view factor (SVF) in an urban environment is to combine urban morphological modeling and computational geometry techniques. Computational methods and tools have been developed to derive SVF from digital surface models (DSMs) and small-scale three dimensional (3D) city models, but they are not well suited for accommodating large-scale 3D city models. In this paper, we present a computational framework (SVFEngine) for deriving SVF from a wide variety of 3D city models, including the recently developed oblique airborne photogrammetry-based 3D city model (OAP3D), which is a highly detailed representation of an urban environment. We compared the SVF estimates computed from a large-scale high-resolution OAP3D with those from a set of street view panoramas at 30 locations. The comparison shows that the SVF estimates obtained using both methods closely match each other with a correlation coefficient of 0.99. The result suggests that high-resolution 3D city models have the potential for deriving accurate SVF estimates. Several examples are presented to show how SVFEngine can be applied in urban environmental modeling and analysis. The source code of SVFEngine is freely available (https://github.com/ljm355/SVFEngine) so it can be fully integrated into a user-defined workflow.

Reconstruction by components for automated updating of 3D city models

3D city models are of interest for various reasons like urban planning, environmental simulations of urban climate and noise pollution, disaster simulations, virtual tourism, virtual-heritage conservation, etc. To create and update large-scale 3D city models efficiently, automated approaches to 3D reconstruction are in great demand. Aside from efficiency, reliability and flexibility are of crucial importance. The derived reconstruction results should be reliable in that they correspond to the observed buildings in both their geometry and their structural topology. Flexibility should ensure the derivation of 3D reconstructions for the most common urban building structures without being limited in descriptive power to only some specific building types. To ensure efficiency, reliability, and flexibility of automated 3D building reconstruction, we propose an approach that combines two paradigms. First, we employ the fusion of information derived from different sensors and map data from a geographic information system. Second, we employ a semantic and component-based approach to model and reconstruct complex buildings. The derived geometrical and semantic building description is utilized within a spatial information system to support spatial and semantic queries for the maintenance and updating of the derived 3D city models.

LARGE SCALE URBAN RECONSTRUCTION FROM REMOTE SENSING IMAGERY

Abstract. Automatic large-scale stereo reconstruction of urban areas is increasingly becoming a vital aspect for physical simulations as well as for rapid prototyping large scale 3D city models. In this paper we describe an easily reproducible workflow for obtaining an accurate and textured 3D model of the scene, with overlapping aerial images as input. Starting with the initial camera poses and their refinement via bundle adjustment, we create multiple heightmaps by dense stereo reconstruction and fuse them into one Digital Surface Model (DSM). This DSM is then triangulated, and to reduce the amount of data, mesh simplification methods are employed. The resulting 3D mesh is finally projected into each of the input images to obtain the best fitting texture for each triangle. As verification, we provide visual results as well as numerically evaluating the accuracy by comparing the resulting 3D model against ground truth generated by aerial laser scanning (LiDAR).

Rich point clouds in virtual globes – A new paradigm in city modeling?

In this paper, we discuss the emergence of a new paradigm in 3D city modeling, which we will call the rich point cloud paradigm and which is largely based on dense and semantically rich 3D point clouds. We first present existing modeling paradigms, namely the explicit modeling of 3D geometry and image-based city modeling, and discuss their strengths and shortcomings. Key elements of the new modeling paradigm include the following: (1) treatment of 3D point clouds with increasingly rich geometric, radiometric and semantic properties as actual urban 3D models and not just as an input to the traditional 3D modeling process, (2) potential for fully automatically capturing and generating large scale 3D city models, (3) capturing rich detail of the micro- to meso-scale environment from a ground-based ‘human’ perspective supporting numerous drive-through and walk-through application scenarios and (4) possibility to start from fully automatically derived simple visual models and to gradually enhance and enrich them on an as-needed basis. We contend that virtual globes have a key role in fully exploiting the potential of the new paradigm. These arguments are supported with the implementation of a proof of concept prototype based on the i3D virtual globe technology and with initial observations from tests with real-world data sets. Finally, we identify a number of future research issues and challenges that are triggered by the new paradigm.

An Automated Method for Large-Scale, Ground-Based City Model Acquisition

In this paper, we describe an automated method for fast, ground-based acquisition of large-scale 3D city models. Our experimental set up consists of a truck equipped with one camera and two fast, inexpensive 2D laser scanners, being driven on city streets under normal traffic conditions. One scanner is mounted vertically to capture building facades, and the other one is mounted horizontally. Successive horizontal scans are matched with each other in order to determine an estimate of the vehicle's motion, and relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques. Specifically, the final global pose is obtained by utilizing an aerial photograph or a Digital Surface Model as a global map, to which the ground-based horizontal laser scans are matched. A fairly accurate, textured 3D cof the downtown Berkeley area has been acquired in a matter of minutes, limited only by traffic conditions during the data acquisition phase. Subsequent automated processing time to accurately localize the acquisition vehicle is 235 minutes for a 37 minutes or 10.2 km drive, i.e. 23 minutes per kilometer.