Delimiting the building heights in a city from the shadow in a panchromatic SPOT-image—Part 1. Test of forty-two buildings

Many researchers have delimited objects' heights, mainly heights of the Earth surface for topographic mapping or digital elevation model (DEM), with SPOT-images by using stereo observe-systems. In this paper, we try to delimit buildings' heights from the shadow in a panchromatic SPOT-image. The heights of 42 buildings are calculated with this method and checked. The root-mean-square error is 3·69 m. A main inference comes from the blocked shadows.     

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.     

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.     

Ascent and descent rates of high-flying insect migrants determined with a non-coherent vertical-beam entomological radar

Vertical-beam radars employing non-coherent transmission and reception provide an effective means of detecting insects migrating at heights of hundreds of metres. Current units can determine the insects’ horizontal velocities, orientations, and some characteristics indicative of the targets’ identities; however, they are not able to detect the vertical component of the targets’ motion. It has generally been presumed that more sophisticated radar technology, employing Doppler processing and requiring coherent operation, would be needed to observe ascent and descent of targets, but we show here that with modern, high-speed, data-acquisition technology it is possible to determine these quantities from the echo signals produced by current non-coherent transceivers. A specially developed data-analysis algorithm that extracts precise target ranges from each of the radar’s pulses and calculates averages over multiple samples forms an essential component of this new capability. An upgraded Insect Monitoring Radar (‘IMRU’) incorporating these elements can estimate target heights with a precision of 0.13 m up to heights of 1 km (and 0.2 m from 1 to 2.5 km), and it does so every 0.13 s. This allows ascent and descent rates to be estimated to a precision of 0.4 m s^?1 at lower altitudes and to 0.1 m s^?1 at the greatest heights, where the broader beam produces echoes with longer duration. Brief case studies are presented that show IMRU observations of ascending and descending insects in daytime convective thermals, around dusk (when nocturnal migration commences with a take-off flight), during an established nocturnal migration, and at the leading edge of a mesofront. Some limitations and potential biases of the method are identified, and its utility relative to alternative methods is considered.     

Detection of collapsed buildings caused by the 1999 Izmit,Turkey earthquake through digital analysis of post-event aerial photographs

In this study, the post-earthquake aerial photographs were digitally processed and analysed to detect collapsed buildings caused by the Izmit, Turkey earthquake of 17 August 1999. The selected area of study encloses part of the city of Golcuk, which is one of the urban areas most strongly hit by the earthquake. The analysis relies on the idea that if a building is collapsed, then it will not have corresponding shadows. The boundaries of the buildings were available and stored in a Geographical Information System (GIS) as vector polygons. The vector building polygons were used to match the shadow casting edges of the buildings with their corresponding shadows and to perform analyses in a building-specific manner. The shadow edges of the buildings were detected through a Prewitt edge detection algorithm. For each building, the agreement was then measured between the shadow producing edges of the building polygons and the thresholded edge image based on the percentage of shadow edge pixels. If the computed percentage value was below a preset threshold then the building being assessed was declared as collapsed. Of the 80 collapsed buildings, 74 were detected correctly, providing 92.50% producer's accuracy. The overall accuracy was computed as 96.15%. The results show that the detection of the collapsed buildings through digital analysis of post-earthquake aerial photographs based on shadow information is quite encouraging. It is also demonstrated that determining the optimum threshold value for separating the collapsed from uncollapsed buildings is important.     

Large-scale parameterization of 3D building morphology in complex urban landscapes using aerial LiDAR and city administrative data

The form and function of the modern city are defined by the three-dimensional contours of the built environment. The morphology of the urban landscape has significant implications for a city's sustainability, efficiency, and resilience. With advancements in remote sensing, especially airborne Light Detection and Ranging (LiDAR), the potential exists to model urban topography at an unprecedented spatial resolution and granularity and extract previously unavailable characteristics of individual buildings. In this study, we demonstrate the application of point-based voxelization techniques to extract design parameters in complex urban environments at unprecedented scale using New York City, and its more than 1,000,000 buildings, as a test case. Covering approximately 800 km², we develop a 1 m² resolution Digital Surface Model (DSM) derived from aerial LiDAR point cloud data, together with city administrative records, to calculate building massing, height, volume, exposed surface area, and compactness ratios for every building in the City. The proposed scalable approach creates a significant opportunity for city administrators, urban planners, architectural engineers, and building designers to understand the relationship between urban morphology and a range of infrastructure and environmental systems.     

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.     

An elevation difference model for building height extraction from stereo-image-derived DSMs

This article investigates the scale issue of inaccurate elevation around buildings in digital surface models (DSMs) and its application in building height estimation. DSMs derived from a single pair of optical stereo images are affected by occlusions and shadows, which lead to indistinct building borders in the DSM. To explore the parameters of how elevation changes in such inaccurate DSMs around buildings, a ‘building–ground elevation difference model’ (EDM) has been designed in this study. This model describes the trend of elevation differences between a building and its neighbours in order to find a stable ground elevation and to estimate actual building height. The EDM is discussed in application to both flat and sloped ground situations. Experiments on two study sites using the proposed model demonstrate that the estimated height at rooftop points can be comparable to light detection and ranging data with respect to rooftop height estimation, which outperforms the conventional filtering method. Furthermore, the proposed semi-variogram model also sheds light on the scale issue of features in DSMs of different spatial resolutions.     

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.     

基于邻域总变分和势直方图函数的高分辨率遥感影像建筑物提取

针对现在的高分辨率遥感影像建筑物识别与提取方法存在的准确率低及数据要求严格等问题,提出一种基于邻域总变分（NTV）和势直方图函数（PHF）的方法。首先,计算遥感影像各像元的加权邻域总变分似然函数取值,并进行区域生长分割,将矩形度和长宽比作为约束条件提取候选建筑物;然后,进行阴影自动提取;最后,利用数学形态学对阴影进行处理,计算处理后的阴影和候选建筑物之间的邻接关系得到建筑物,并用最小外接矩形对其边界进行拟合。为了验证所提算法的有效性,选取深圳市PLEIADES影像中9幅具有代表性的子影像进行实验。实验结果表明,所提方法的平均查准率和平均查全率分别达到97.71%和84.21%,与水平集和基于颜色不变性特征两种建筑物提取方法相比,在总体性能F₁上具有10%以上的提高。     

Building‐based damage detection due to earthquake using the watershed segmentation of the post‐event aerial images

This paper presents an approach for detecting the damaged buildings due to earthquake using the watershed segmentation of the post‐event aerial images. The approach utilizes the relationship between the buildings and their cast shadows. It is based on an idea that if a building is damaged, it will not produce shadows. The cast shadows of the buildings are detected through an immersion‐based watershed segmentation. The boundaries of the buildings are available and stored in a GIS as vector polygons. The vector‐building boundaries are used to match the shadow casting edges of the buildings with their corresponding shadows and to perform assessments on a building‐specific manner. For each building, a final decision on the damage condition is taken, based on the assessments carried out for that building only. The approach was implemented in Golcuk, one of the urban areas most strongly hit by the 1999 Izmit, Turkey earthquake. To implement the approach, a system called the Building‐Based Earthquake Damage Assessment System was developed in MATLAB. Of the 284 buildings processed and analysed, 229 were correctly labelled as damaged and undamaged, providing an overall accuracy of 80.63%.     

Energy load superposition and spatial optimization in urban design: A case study

Building energy consumption accounts for a large portion of total energy-use in a city or a regional district. However, energy load spatial distribution has seldom been considered during urban design phase. And energy conservation and energy efficiency measures pay more attention to individual building than buildings in a district or regional space as a whole. If buildings with different functions are mixed together and share same energy system, the savings on system capacity and peak electricity load can be significant. In this paper, a load superposition concept is proposed. The term ‘superposition’ refers to overlapping of energy demand load curves from different buildings and so that the total peak is smaller than the sum of individual peaks. Three spatial optimization methods of demand side load management and three different schemes of energy systems are proposed in this paper. And economic analysis is recommended to evaluate the different energy systems. The applicability of different approaches and the significance of load superposition was analyzed and elaborated through a case study to offer planners a feasible way for evaluating the potential of load spatial optimization.     

A machine learning-based method for the large-scale evaluation of the qualities of the urban environment

Given the present size of modern cities, it is beyond the perceptual capacity of most people to develop a good knowledge about the qualities of the urban space at every street corner. Correspondingly, for planners, it is also difficult to accurately answer questions such as ‘where the quality of the physical environment is the most dilapidated in the city that regeneration should be given first consideration’ and ‘in fast urbanising cities, how is the city appearance changing’. To address this issue, in the present study, we present a computer vision method that contains three machine learning models for the large-scale and automatic evaluation on the qualities of the urban environment by leveraging state-of-the-art machine learning techniques and wide-coverage street view images. From various physical qualities that have been identified by previous research to be important for the urban visual experience, we choose two key qualities, the construction and maintenance quality of building facade and the continuity of street wall, to be measured in this research. To test the validity of the proposed method, we compare the machine scores with public rating scores collected on-site from 752 passers-by at 56 locations in the city. We show that the machine learning models can produce a medium-to-good estimation of people's real experience, and the modelling results can be applied in many ways by researchers, planners and local residents.     

Automatic building height extraction by volumetric shadow analysis of monoscopic imagery

This article presents a new approach to automatic extraction of building heights from monoscopic urban scenes. A volumetric shadow analysis (VSA) method was proposed previously for extraction of 3D building information (height, shape, and footprint location) and for handling occluded building footprints or shadows. It determined building heights by adjusting building height manually until the projected shadows generated for an assumed height and actual shadows in the image matched. In this article, we propose an intelligent scheme based on the VSA for automatic building height extraction. We achieve this by checking the location change of projected shadow lines with respect to the actual shadow regions while building heights are increased incrementally. In this article, the performance of the proposed automatic height extraction was compared to that of manual extraction. The method was first applied to IKONOS, KOMPSAT-2, QuickBird, and Worldview-1 images with manually extracted building roofs. The root mean square error (RMSE) of building heights was under 3 m by automatic height extraction and 2 m by manual extraction. The RMSE of building footprint location was close to twice that of image ground sample distance (GSD) by automatic height extraction and under twice that of image GSD by manual extraction. These results support the capability of the proposed method in automatic height extraction from a single image efficiently and accurately, and in handling occluded building footprints and shadows. Second, the method was combined with an existing roof extraction method and tested for automated building roof extraction. The results showed that the proposed method can also provide a powerful cue for automatic building roof extraction from a single image.     

Towards the generation of 3D OpenStreetMap building models from single contributed photographs

3D city models are valuable and useful for many experts using this information for a wide array of environmental and sustainable analyses and services. To produce 3D city models, conventional production processes are typically conducted by authoritative mapping agencies, which mostly rely on aerial photogrammetry and laser scanning. Consequently, obtaining public domain 3D city models is challenging and limited, where the majority of open data is collected and mapped by participatory mapping driven communities. These are still limited to 2D data collection proficiencies due to the used mapping infrastructures and technological limitations. Thus, the 3rd (height) dimension is mostly missing from these maps and models, resulting in the fact that public domain 3D city models are still limited, and only scarcely used for environmental applications. Perhaps one of the most important features in 3D city models are the buildings, since they serve as a major geospatial element in many environmental applications. Our objective is to use a single contributed photograph and OpenStreetMap vector data to precisely calculate the photographed building height, and add this data to the OpenStreetMap map to enable the creation of open source Level of Detail 1 (LoD1) city models. To this end, we have developed a Newton's-based method in optimization to accurately calculate building heights from single contributed photographs taken by citizens using smartphones or tablets. An Android app, OpenStreetHeight, is developed to carry out the experiments. Based on the various medium-height buildings that were photographed using the app and processed using the developed algorithms we received accurate building height values. When compared to reliable reference field measurements, the average height mean absolute error was 30 cm. Combined with the OpenStreetMap footprint vector data, we were able to produce an average LoD1 volume mean absolute error of less than 5%, satisfying the CityGML standard quality. This research presents a framework for a semi-automatic crowdsourced user-generated content calculation of OpenStreetMap building heights, and the creation of reliable and accurate LoD1 building models, as a first step to enhance the already established 2D OpenStreetMap map infrastructure to the 3D domain. This enables expanding the use of OpenStreetMap as a comprehensive and detailed representation of our urban environment for various environmental and sustainable applications and analyses.     

Appearance Stylization of Manhattan World Buildings

We propose a method that generates stylized building models from examples (Figure 1 ). Our method only requires minimal user input to capture the appearance of a Manhattan world (MW) building, and can automatically retarget the captured ‘look and feel’ to new models. The key contribution is a novel representation, namely the ‘style sheet’, that is captured independently from a building's structure. It summarizes characteristic shape and texture patterns on the building. In the retargeting stage, a style sheet is used to decorate new buildings of potentially different structures. Consistent face groups are proposed to capture complex texture patterns from the example model and to preserve the patterns in the retarget models. We will demonstrate how to learn such style sheets from different MW buildings and the results of using them to generate novel models.     

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。     

Detecting horizontal and vertical urban growth from medium resolution imagery and its relationships with major socioeconomic factors

Urban growth consists of horizontal and vertical expansions. An integrative framework for estimating horizontal and vertical expansions of city urban areas using Landsat images was presented. It includes following steps: (1) a spectrum-based classifier (here Support Vector Machine) is first used to preclassify Landsat images; (2) the spectral similarity-enhanced Markov chain random field cosimulation model is then applied to postclassify the preclassified images and detect building shadows; and (3) a morphological operator based on spatial logic reasoning is used to estimate mid-rise or taller buildings (MTBs) from detected shadows. Both horizontal urban growth and vertical urban growth in the main city area of Guangzhou for the time period of 1993–2013 were detected. The accuracy of identified MTBs by shadows was validated to be 78.1% on average for 2013. The case study indicates that Guangzhou had undergone both horizontal and vertical urban growth from 1993 to 2013, and vertical urban growth followed horizontal urban growth successively. The relationships between the horizontal and vertical urban growth and three major socioeconomic factors during the studied period were analysed. Results indicate that both the total area of built-up areas and the total area of detected MTBs are significantly correlated with population density, real gross domestic product, and fixed investment (i.e. investment in fixed assets such as land, buildings), respectively. While population density is the major driving force of horizontal urban expansion, fixed investment is the major driving force of vertical urban expansion for the city as a whole. Although the method is not perfect currently in detecting MTBs in various situations and the case study is mainly exploratory, the proposed framework and the case study can be helpful in quantitatively exploring the horizontal urban growth and vertical urban growth of a city and their causes.     

基于结构信息的城市建筑物测量方法

提高城市建筑物测量过程的自动化程度是航空摄像测量研究的主要问题之一。文中提出了一种基于结构信息的城市建筑物测量方法。首先根据城市建筑物的结构信息,用几个相互独立的参数来描述建筑物的理想模型,并定义能推导出模型参数的图像变量,然后组合利用左右图像中各种信息获得图像变量的初始值,最后在一定准则下寻求城市建筑物的最佳测量位置。该文以平顶矩形建筑物为例详细讨论了它的一种实现算法。实验结果表明,这种方法与传统方法相比较,有较高的自动化程度,且测量精度能满足现有航空摄影测量的要求,有较大的发展和应用潜力。