Detection and typification of linear structures for dynamic visualization of 3D city models

Cluttering is a fundamental problem in 3D city model visualization. In this paper, a novel method for removing cluttering by typification of linear building groups is proposed. This method works in static as well as dynamic visualization of 3D city models. The method starts by converting building models in higher Levels of Details (LoDs) into LoD1 with ground plan and height. Then the Minimum Spanning Tree (MST) is generated according to the distance between the building ground plans. Based on the MST, linear building groups are detected for typification. The typification level of a building group is determined by its distance to the viewpoint as well as its viewing angle. Next, the selected buildings are removed and the remaining ones are adjusted in each group separately. To preserve the building features and their spatial distribution, Attributed Relational Graph (ARG) and Nested Earth Mover’s Distance (NEMD) are used to evaluate the difference between the original building objects and the generalized ones. The experimental results indicate that our method can reduce the number of buildings while preserving the visual similarity of the urban areas.     

面向智慧城市的3维城市在线可视化

目的 3维城市可视化是智慧城市信息显示的基础,对城市信息的实时准确传递起着重要作用。而现有的3维城市可视化方法和系统存在两点局限性:一是数据模型不适合于海量建筑物显示;二是对整个城市采用单一绘制方式,而建筑物的纹理、结构、高度等特征相似,绘制结果容易引起视觉混淆,为此提出一种基于人类感知理论的3维城市在线可视化技术。方法在预处理阶段,系统采用建筑综合算法建立3维城市建筑物的多分辨率表示;在运行时刻,系统根据用户交互,自适应选择建筑物相应的层次进行显示。结果采用几个3维城市数据对系统进行了测试,实验结果证明,该系统有效地提高了3维城市绘制效率。Leverkusen城市的5 530座建筑物,绘制效率达到19.4帧/s。结论基于感知的3维城市多分辨率表示,有效提高了3维城市系统的显示效率以及用户获取信息的效率,同时提高了用户的交互效率。     

Generating 3D city models without elevation data

Elevation datasets (e.g. point clouds) are an essential but often unavailable ingredient for the construction of 3D city models. We investigate in this paper to what extent can 3D city models be generated solely from 2D data without elevation measurements. We show that it is possible to predict the height of buildings from 2D data (their footprints and attributes available in volunteered geoinformation and cadastre), and then extrude their footprints to obtain 3D models suitable for a multitude of applications. The predictions have been carried out with machine learning techniques (random forests) using 10 different attributes and their combinations, which mirror different scenarios of completeness of real-world data. Some of the scenarios resulted in surprisingly good performance (given the circumstances): we have achieved a mean absolute error of 0.8m in the inferred heights, which satisfies the accuracy recommendations of CityGML for LOD1 models and the needs of several GIS analyses. We show that our method can be used in practice to generate 3D city models where there are no elevation data, and to supplement existing datasets with 3D models of newly constructed buildings to facilitate rapid update and maintenance of data.     

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.     

Automatic classification of building types in 3D city models

This article presents a classifier based on Support Vector Machines (SVMs), an advanced machine learning method for semantic enrichment of coarse 3D city models by deriving the building type. The information on the building type (detached building, terraced building, etc.) is essential for a variety of relevant applications of 3D city models like spatial marketing, real estate management and marketing, and for visualization. The derivation of the building type from coarse data (mainly 2D footprints, building heights and functions) seems impossible at first sight. However it succeeds by incorporating the spatial context of a building. Since the input data can be derived easily and at very low cost, this method is widely applicable. Nevertheless, as with all supervised learning algorithms, obtaining labelled training data is very time-consuming. Herewith, we provide a method which uses outlier detection and clustering methods to support users in efficiently and rapidly obtaining adequate training data.     

利用SPOT图象阴影提取城市建筑物高度及其分布信息

在分析SPOT卫星图象阴影与建筑物实际高度关系的基础上,阐述了依据图象建筑物来估算城市建筑物高度的原理和方法,进而探讨了以数据融合为手段的,从SPOT全色图象中准确界定阴影范围的方法,并以此为基础,研究出了一种基于图象阴影特征的城市建筑物高分级及其分布信息自生成技术,在以北京市为例的试验中,建筑物高度分级结果的抽样验证准确率达80％以上,显示出卫星遥感在城市应用方面的巨大潜力。     

3D city models for urban farming site identification in buildings

Studies have suggested that there is farming potential in urban residential buildings. However, these studies are limited in scope, require field visits and time-consuming measurements. Furthermore, they have not suggested ways to identify suitable sites on a larger scale let alone means of surveying numerous micro-locations across the same building. Using a case study area focused on high-rise buildings in Singapore, this paper examines a novel application of three-dimensional (3D) city models to identify suitable farming micro-locations (level and orientation) in residential buildings. We specifically investigate whether the vertical spaces of these buildings comprising outdoor corridors, façades and windows receive sufficient photosynthetically active radiation (PAR) for growing food crops and do so at a high resolution. We also analyze the spatio-temporal characteristics of PAR, and the impact of shadows and different weather conditions on PAR in the building. Environmental simulations on the 3D model of the study area indicated that the cumulative daily PAR or Daily Light Integral (DLI) at a location in the building was dependent on its orientation and shape, sun's diurnal and annual motion, weather conditions, and shadowing effects of the building's own façades and surrounding buildings. The DLI in the study area generally increased with building's levels and, depending on the particular micro-location, was found suitable for growing moderately light-demanding crops such as lettuce and sweet pepper. These variations in DLI at different locations of the same building affirmed the need for such simulations. The simulations were validated with field measurements of PAR, and correlation coefficients between them exceeded 0.5 in most cases thus, making a case that 3D city models offer a promising practical solution to identifying suitable farming locations in residential buildings, and have the potential for urban-scale applications.     

图形图像融合的海量建筑绘制

目的 城市3维模型数据海量且结构复杂,缺乏一个高效完善的可视化系统往往是影响数字城市应用的瓶颈之一。通常利用多层次细节（LOD）与调度算法减少每一帧绘制的数据量来提高绘制效率,当场景规模足够大时,即使采用复杂的优化算法也难以取得较好的效果。为此,本文在传统算法基础上,提出一种图形图像融合的海量建筑物场景绘制方法。方法 提出并采用视域分级绘制策略,将视椎体平行分割为感兴趣区域、次感兴趣区域和非感兴趣区域,感兴趣区域采用图形实时绘制方法,使用离屏渲染技术将次感兴趣和非感兴趣区域绘制在纹理图像中,每一帧绘制完成后将二者进行顾及深度信息的融合,实现完整场景渲染。结果 使用公开的纽约市区CityGML文件作为实验数据,数据包含了118 195个LOD1和LOD2级别的建筑物模型。分别构建多组不同建筑数量的场景进行帧率统计实验,绘制帧率都达到20帧/s以上。算法实现了视觉无损失的场景完整渲染,并与Cesium平台进行对比实验,证明算法有效且系统运行流畅。结论 图形图像融合的绘制方法,既保持了图形渲染的漫游连续性,同时也具有图像渲染的场景复杂度无关的优点。实验结果表明,针对大规模的低分辨率建筑模型场景,算法可以有效提高系统的渲染能力,在性能相对较低的硬件条件下也能实现海量建筑物实体模型的流畅漫游,并达到视觉无损失的场景完整绘制。     

Automatic urban modeling using volumetric reconstruction with surface graph cuts

The demand for 3D city-scale models has been significantly increased due to the proliferation of urban planning, city navigation, and virtual reality applications. We present an approach to automatically reconstruct buildings densely spanning a large urban area. Our method takes as input calibrated aerial images and available GIS meta-data. Our computational pipeline computes a per-building 2.5D volumetric reconstruction by exploiting photo-consistency where it is highly sampled amongst the aerial images. Our building surface graph cut method overcomes errors of occlusion, geometry, and calibration in order to stitch together aerial images and yield a visually coherent texture-mapped result. Our comparisons show similar quality to the manually modeled buildings of Google Earth, and show improvements over naive texture mapping and over space-carving methods. We have tested our algorithms with a 12 sq km area of Boston, MA (USA), using 4667 images (i.e., 280 GB of raw image data) and producing 1785 buildings.     

建筑物多尺度三维语义建模研究

针对现有的一些三维建模方法强调单一尺度几何模型构建,缺乏多细节层次(LoD)和语义信息表达,互操作性不强等问题,以国际开放标准CityGML为基础,利用矢量拉伸、基于几何特征和规则的语义信息提取、几何映射和语义映射等技术研究了建筑物多尺度几何语义一体化模型构建方法.采用开源Cesium库开发了一套基于WebGL的建筑物...     

A two-level topological model for 3D features in CityGML

CityGML, as the standard for the representation and exchange of 3D city models, contains rich information in terms of geometry, semantics, topology and appearance. With respect to topology, CityGML adopts the XLink approach to represent topological relationships between different geometric aggregates or thematic features; however, it is limited to shared objects. This paper proposes a two-level model for representing 3D topological relationships in CityGML: high-level (semantic-level) topology between semantic features and low-level (geometric-level) topology between geometric primitives. Five topological relationships are adopted in this model: touch, in, equal, overlap and disjoint. The semantic-level topology is derived from the geometric-level topology on the basis of the shared geometric primitives. To maintain the 3D topology, topological consistency rules are presented. An Application Domain Extension, called TopoADE, is proposed for the implementation of the topological model. The TopoADE consists of three modules: Topology, Feature and Geometry. Finally, 3D city models with LoD1 to LoD4 are used to test this model. Experimentation on those data sets indicates a validation of the proposed topological model in CityGML.     

基于无人机机载激光雷达的海岛建筑物高度测算技术

当前技术在测量海岛建筑物高度时,得到的激光点云数据质量差,测算结果不准确。为了解决上述问题,基于无人机激光雷达研究了一种新的海岛建筑物高度测算技术。利用无人机自动控制技术对海岛建筑物进行多点多角度照片拍摄,拍摄方案有两种,分别是折线形飞行方案和环绕型飞行方案;在海岛建筑物的不同位置和不同高度设置多个地面影像控制点,通过航拍得到各个海岛区域的特征图像,利用Smart3D建模软件完成建模,将建立的三维模型从图像空间坐标系转换到大地空间坐标系,从而得到海岛建筑物高度。与传统测算技术进行实验对比,结果表明,基于无人机激光雷达的海岛建筑物高度测算技术测算精度高于传统技术,对于海岛管理有很好的促进作用。     

Synoptic mapping of high-rise buildings in urban areas based on combined shadow analysis and scale space processing

A highly automated methodology is described to map locations and heights of high-rise buildings from single high-resolution multi-spectral satellite imagery. The approach involves preliminary shadow detection using the Tsai colour invariant transform and scale space processing to identify candidate building pixels. Application of shadow-building and shadow length constraints led to mapping of the location and height of building candidate objects. The approach has been applied to a winter SPOT 5 scene of Beijing, China. Tests of buildings in a suburban area indicate that a high detection rate (93%) can be achieved for buildings taller than 28 m. A height estimation accuracy of 20 m has also been met for these buildings.     

Complete classification of raw LIDAR data and 3D reconstruction of buildings

LIDAR (LIght Detection And Ranging) data are a primary data source for digital terrain model (DTM) generation and 3D city models. This paper presents a three-stage framework for a robust automatic classification of raw LIDAR data as buildings, ground and vegetation, followed by a reconstruction of 3D models of the buildings. In the first stage the raw data are filtered and interpolated over a grid. In the second stage, first a double raw data segmentation is performed and then geometric and topological relationships among regions resulting from segmentation are computed and stored in a knowledge base. In the third stage, a rule-based scheme is applied for the classification of the regions. Finally, polyhedral building models are reconstructed by analysing the topology of building outlines, building roof slopes and eaves lines. Results obtained on data sets with different ground point density, gathered over the town of Pavia (Italy) with Toposys and Optech airborne laser scanning systems, are shown to illustrate the effectiveness of the proposed approach.     

Automatic generation of high-quality building models from lidar data

Automating data acquisition for 3D city models is an important research topic in photogrammetry. In addition to techniques that rely on aerial images, generating 3D building models from point clouds provided. by light detection and ranging (Lidar) sensors is gaining importance. The progress in sensor technology has triggered this development. Airborne laser scanners can deliver dense point clouds with densities of up to one point per square meter. Using this information, it's possible to detect buildings and their approximate outlines and also to extract planar roof faces and create models that correctly resemble the roof structures. The author presents a method for automatically generating 3D building models from point clouds generated by the Lidar sensing technology.     

Fusion of 3D building models derived from first and last pulse laserscanning data

The work presented in this article is part of a project of collaborative research center 461: “Strong earthquakes”. Geometric modelling of buildings in urban areas, detecting and interpreting their changes is necessary to obtain fast damage information after earthquakes as important input for a disaster management system. Airborne laserscanning was chosen as data basis for this application due to its advantages like extensive independence of weather and lighting conditions, high accuracy and measurement density. Investigations have shown that building models created by means of laserscanning data have some systematic deviations compared to accurate reference models. Modern laserscanning sensors are able to record first signal response (first pulse) and last response (last pulse) simultaneously. Dependent on these modes the derived building models show system inherent differences: a principle enlargement using first pulse mode and an analogous reduction in last pulse mode. Their amount is dependent on the flights parameters and the resolution of the derived data set, here the enlargement was about +1.2 m and the reduction about −1.2 m. It will be demonstrated that a fusion of two models of a building, derived on the basis of first and last pulse data respectively, leads to an enormous accuracy improvement. At this state the fusion process is—according to the used quality assessment—divided into a positioning and height component. An algorithm for the determination of a centerline between the borderlines of the two building models will be presented. This line is used for adjustment of the position. For adjustment of the height, i.e. the roof structure, vertical distances inside corresponding planes of the two models are used to calculate a new adjusted one. The final building modelling is based on the intersection of these adjusted planes. The resulting building models fit much better to their related reference models. The mean positioning deviation decreases to ≈0.25 m, while the deviations in height are estimated between ≈0.02 m (flat roofs) and 0.10 m (steep sloped roofs). This significantly improved accuracy level lies below the accuracy of original laserscanning data and therefore enables, beside the recognition of building damages, a great variety of applications in urban environment like generation of 3D city models, e.g. for planning purposes.     

Fast and robust generation of city-scale seamless 3D urban models

Since the introduction of the concept of “Digital Earth”, almost every major international city has been re-constructed in the virtual world. A large volume of geometric models describing urban objects has become freely available in the public domain via software like ArcGlobe and Google Earth. Although mostly created for visualization, these urban models can benefit many applications beyond visualization including city scale evacuation planning and earth phenomenon simulations. However, these models are mostly loosely structured and implicitly defined and require tedious manual preparation that usually takes weeks if not months before they can be used. Designing algorithms that can robustly and efficiently handle unstructured urban models at the city scale becomes a main technical challenge. In this paper, we present a framework that generates seamless 3D architectural models from 2D ground plans with elevation and height information. These overlapping ground plans are commonly used in the current GIS software such as ESRI ArcGIS and urban model synthesis methods to depict various components of buildings. Due to measurement and manual errors, these ground plans usually contain small, sharp, and various (nearly) degenerate artifacts. In this paper, we show both theoretically and empirically that our framework is efficient and numerically stable. Based on our review of the related work, we believe this is the first work that attempts to automatically create 3D architectural meshes for simulation at the city level. With the goal of providing greater benefit beyond visualization from this large volume of urban models, our initial results are encouraging.     

Delimiting the building heights in a city from the shadow on a panchromatic SPOT-image: Part 2: Test of a complete city

The building heights and the heights' distribution of a city are important information for the research about the small climate of the city, the modelling of the signal transformation between the mobile phones in the city, etc. In this paper a test is shown, in which the building heights and the heights' distribution of a city (about 20 km by 20 km) are delimited from the shadows that are identified on a panchromatic SPOT-image. The identifying precision is 93 per cent by checking 200 identified buildings. The calculated heights of 77 buildings are examined and the root-mean-square error is 6-13 m. The high building density and overlapping of the grey value distribution between ‘shadow’ and ‘tree/bush’ on the image are two main inferences. The test shows that (1) the building heights and the heights' distribution of a city could be roughly delimited from the shadows on a very good panchromatic SPOT-image, and (2) the delimiting precision could meet the demands of the above two researches.     

Productive high-complexity 3D city modeling with point clouds collected from terrestrial LiDAR

To satisfy the needs of photo-realistic and ground-based representation of three-dimensional (3D) city models for a variety of applications, significant efforts have been made to automatically reconstruct detailed 3D building façades from terrestrial LiDAR data. Nonetheless, in real-world applications for high-quality 3D city modeling, three major problems are typically encountered: (1) very low productivity due to fully manual operation, (2) low geometric accuracy of 3D modeling resulting from the process of reducing original LiDAR data, and (3) system failure when importing huge LiDAR data to 3D drawing software. To overcome these limitations, the present study proposes a semi-automatic method entailing a plane component detection based on RANSAC segmentation, boundary tracing of the planar components, and manual drawing of details using the remaining, significantly reduced points. The proposed method was applied to point clouds of various buildings in a high-density area in Korea. In comparison with manual operation, the proposed method was proved to improve modeling productivity in the time-consumption aspect and to facilitate operators’ accurate object drawing. However, for additional automation and completeness of 3D modeling, further study is necessary. The proposed method requires a segmentation algorithm to heuristically determine parameters for the most desirable results as well as to detect curvilinear surfaces in modeling complex and curved façades.     

The use of a virtual city model for assessing equity in access to views

The development of virtual city models has provided novel possibilities for analyses that require consideration of building heights in urban areas. The study was undertaken to explore these possibilities by using the virtual Kyoto model to examine equity in access to views in the Japanese city. A sample of just over 5000 residences was selected by stratifying for population age and affluence. A series of viewsheds were computed to quantify the visibility of a range of environmental amenities (greenspaces, water bodies, historical buildings, mountains) and disamenities (factories and roads). Evidence of inequity in visual amenity was identified, whereby homes in areas with many old people were much less likely to have views of greenspaces and water bodies, although they were also less likely to see factories and roads and were more likely to view mountains. Homes in more affluent areas had better views of greenspaces, historical buildings, and mountains, and were less likely to see factories and water bodies. We discuss the potential of virtual city models for furthering analyses of the urban environment and raise some caveats regarding their use.