Shadow information recovery in urban areas from very high resolution satellite imagery

It is usually quite difficult to extract and recover shadow information in the urban environment from remote sensing imagery. This paper describes the study of precisely detected shadow in satellite images and recovering information from the surface covered in shadow from very high resolution (VHR) satellite imagery.     

Deriving fine-scale socioeconomic information of urban areas using very high-resolution satellite imagery

This article presents a new approach to derive fine-scale socioeconomic information of urban areas using very high resolution satellite data. The rationale behind the method is to use high resolution satellite data, capable of resolving urban morphology details, to derive a classification of the image. Thus, it is assumed that there is a relationship between the socioeconomic profile and the urban morphology of an area in terms of availability of green areas, sport facilities, private swimming pools or pavement conditions. The method is tested using a case study of Lima, Peru. Using a sample of ground data, a neural network classifier was applied to a pre-classified image in which entropy had been used to mask extensive, non-built up areas that would otherwise have inserted spurious information into the classifier. The result shows a high correlation (0.70 R ²) when compared with validation data. The good performances also show that a physiographic satellite view of the city reflects the socioeconomic layout of their inhabitants, thus making remote sensing a complementary tool for social research and urban planning. While the parameterization of the problem may differ from one area to another, it is shown that an a priori choice of a few parameters may help to automatically characterize large areas in social terms, thus allowing social inequality and its evolution to be mapped in those areas with limited availability of data. In order to make the method widely applicable, the possibilities and limitations of applying the procedure to other large cities are discussed.     

Structural analysis and optimization of tall buildings connected with skybridges and atria

Multi-level skybridges and atria between tall buildings make possible a new urban paradigm that has the potential to dramatically reduce resource consumption, air pollution, and traffic accidents. Structural analysis and optimization methods and results are presented for tall buildings connected with roller or hinge-skybridges at multiple levels and atria between buildings. Generic building systems were studied with controlled factors such as number of buildings, equal-height vs. variable-height, skybridge connection type, site seismicity and wind intensity, and presence/absence of skybridges and atria. The 48 optimization problems held all other factors constant such as building width, shape, configuration, and spacing. A simplified lateral load analysis model is described which makes optimization of large systems possible. The simplified lateral load analysis model was compared to a finite element model for accuracy. An optimization strategy was developed involving gradient and evolutionary algorithms. Results show that atria and hinge-connected skybridges reduced the total structural volume by as much as 10 %.     

Optimum positioning of actuators in tall buildings using genetic algorithm

An effective method for determining the optimal position of actuators in tall buildings using genetic algorithm is introduced through the formulation of a discrete and non-linear optimization problem. The simulation study is carried out for a 16-story building under 18 different earthquake excitations. The effects of different earthquake excitations on the optimal placement of actuators and on the proposed optimization algorithm are thus examined.     

Behavior of tall buildings with mixed use of rigid and semi-rigid connections

Semi-rigid connections are rarely used in tall building frames. In this paper, the mixed use of rigid connections with semi-rigid connections for tall building frames as a way to reduce costs is investigated. Using the three-parameter power model for semi-rigid connections, a four-bay eight-story frame is numerically analyzed with four combinations of mixed use of rigid and semi-rigid connections. It is shown that normalized building drift Δ/H can be kept under 1/400 by properly selecting the combination of rigid and semi-rigid connections.     

A simulation study of energy consumption by elevators in tall buildings

The problem of estimating the energy requirements of elevators in tall buildings is shown to be dependent on the integration of functions of random variables. In addition, the period of integration is also random. Hence, a discrete-continuous simulation language, GASP IV, is employed as a method of solving the equations which describe the model. The model is applied to estimating the energy requirements of a building which contains both apartments and commercial facilities. Amongst the topics studied are the effects of elevator speeds and counterweight values on energy consumption, and also the amount of energy available for regeneration. Although the prediction of energy consumption is not considered to be an Industrial Engineering function, Industrial Engineering concepts and tools are used in modelling the problem.     

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.     

Satellite radar anisotropy observed in urban areas

QuikSCAT backscatter is generally higher over urban areas than surrounding vegetated areas. Azimuthal anisotropy has been observed over some urban areas, but the strength of the azimuthal anisotropy in the urban backscatter signal has not been well quantified. This study investigates radar azimuthal anisotropy in urban areas. QuikSCAT L1B σ⁰ observations are compared for urban, non-urban, and uninhabited regions to identify the magnitude and possible causes of anisotropic responses. The possible cause of azimuthal variations (AVs) in the data is the presence of corner reflectors, resulting from urban infrastructure and land use, including buildings, roads, and road structure. Backscatter characteristics for each urban area are shown to be closely related to road orientation and organization. Each region is found to have a unique backscatter signal and azimuthal response.     

基于多尺度的城区机载点云滤波方法研究

针对城市地区机载激光扫描数据（ALS）中提取数字地面模型这一问题,提出了一种基于多尺度的由粗到细的滤波算法。通过计算每个点与其周围一定邻域内激光点的几何特征值关系,将点云粗分类为平面点、边缘点和离散点;对平面点进行区域跟踪,利用强度方差将平面点分类为地面点、建筑物点以及未确定类别点;对地面点构建Delaunay三角网,反向分析未确定点以及边缘点来加密地面点集。通过实验验证了该算法对城市地区滤波的有效性。     

Data-driven approaches for emissions-minimized paths in urban areas

Concerns about air quality and global warming have led to numerous initiatives to reduce emissions. In general, emissions are proportional to the amount of fuel consumed, and the amount of fuel consumed is a function of speed, distance, acceleration, and weight of the vehicle. In urban areas, vehicles must often travel at the speed of traffic, and congestion can impact this speed particularly at certain times of day. Further, for any given time of day, the observations of speeds on an arc can exhibit significant variability. Because of the nonlinearity of emissions curves, optimizing emissions in an urban area requires explicit consideration of the variability in the speed of traffic on arcs in the network. We introduce a shortest path algorithm that incorporates sampling to both account for variability in travel speeds and to estimate arrival time distributions at nodes on a path. We also suggest a method for transforming speed data into time-dependent emissions values thus converting the problem into a time-dependent, but deterministic shortest path problem. Our results demonstrate the effectiveness of the proposed approaches in reducing emissions relative to the use of minimum distance and time-dependent paths. In this paper, we also identify some of the challenges associated with using large data sets.     

倾斜航空影像的城区DSM生成

目的 以五方向倾斜航空影像为数据源开展城市数字表面模型(UDSM)半全局匹配(SGM)生成研究.方法 主要包括两方面:1) 路径受限的SGM金字塔匹配.以数量众多、分布均匀的倾斜影像空中三角测量连接点为约束对SGM匹配代价路径进行分段“纠正”,以连接点薄板样条变换(TPS)生成的良好初始视差为基础实施金字塔匹配,提高SGM可靠性并实现路径搜索加速;2) 基于遮挡分析的UDSM多视融合.以倾斜影像垂直视图UDSM为主体,将其他视图UDSM投影到垂直视图进行遮挡判断并根据其投影坐标分成3部分:遮挡点集、与垂直UDSM重叠的冗余点集、与垂直UDSM无重叠的非冗余点集,其中经过有效范围剔除的遮挡点集和非冗余点集将用于弥补垂直UDSM在立面或地面局部遮挡区域缺失的空间信息.结果 针对某城市局部区域的UDSM生成实验显示,对存在透视失真、遮挡及不同基线条件的五方向倾斜航空像对,本文方法匹配生成的各像对UDSM质量均比传统SGM算法表现出色,融合后的UDSM点云分布更均匀、空间信息更完整,明显优于传统UDSM.结论 以空中三角测量可靠连接点为路径约束并采用具有增大“拉入”范围能力的金字塔匹配策略,较好地解决了SGM因倾斜影像透视失真严重,视差搜索范围大而导致的匹配代价函数局部最优,错误匹配沿代价路径向后传播以及实时存储、计算开销大等应用问题,保障了倾斜影像的高质量密集匹配并明显提升计算效率;以垂直视图为主体的UDSM多视融合遮挡分析,充分考虑了同一目标区域多视UDSM空间信息既互补又存在高度冗余的特点,在有效去除冗余点云的同时,利用4个不同视图方向的UDSM来弥补垂直UDSM在立面以及局部地面、建筑物屋顶面空间信息的不足,为后续城市“真”正射影像制作及3维城市建模奠定了基础.     

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.     

A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms.     

School location methodology in urban areas of developing countries

This study is concerned with the location of primary public schools. The public system should have the capacity to satisfy all the demand, although students may choose between public and private schools if they can afford the corresponding costs. A number of factors, such as questionable education quality, limited capacity, poor location and social preferences, secure a participation of about 30% to the private school system. The purpose of this study is both the evaluation of the existing public school network and a relocation proposal. The result of the former was the identification of areas with shortage and excess in school offer. The latter suggests school relocation using capacitated and uncapacitated models. ArcView, a software of the geographic information system (GIS) family, was employed, allowing the efficient handling of large problems and improving the presentation and evaluation of the findings. This methodology was applied to the primary public school network in the area of Vitoria, a state capital located in the southeast region of Brazil with about 300,000 inhabitants. Finally, the practical use of this study and its importance for planning purposes are discussed.     

Correcting brightness gradients in hyperspectral data from urban areas

The analysis of airborne hyperspectral data is often affected by brightness gradients that are caused by directional surface reflectance. For line scanners these gradients occur in across-track direction and depend on the sensor's view-angle. They are greatest whenever the flight path is perpendicular to the sun-target-observer plane. A common way to correct these gradients is to normalize the reflectance factors to nadir view. This is especially complicated for data from spatially and spectrally heterogeneous urban areas and requires surface type specific models. This paper presents a class-wise empirical approach that is adapted to meet the needs of such images.Within this class-wise approach, empirical models are fit to the brightness gradients of spectrally pure pixels from classes after a spectral angle mapping (SAM). Compensation factors resulting from these models are then assigned to all pixels of the image, both in a discrete manner according the SAM and in a weighted manner based on information from the SAM rule images. The latter scheme is designed in consideration of the great number of mixed pixels.The method is tested on data from the Hyperspectral Mapper (HyMap) that was acquired over Berlin, Germany. It proves superior to a common global approach based on a thorough assessment using a second HyMap image as reference. The weighted assignment of compensation factors is adequate for the correction of areas that are characterized by mixed pixels.A remainder of the original brightness gradient cannot be found in the corrected image, which can then be used for any subsequent qualitative and quantitative analyses. Thus, the proposed method enables the comparison and composition of airborne data sets with similar recording conditions and does not require additional field or laboratory measurements.     

Survey of emissivity variability in thermography of urban areas

This study investigates the effects of roof emissivity variation of aerial thermogram images. Thermograms have been used to detect heat loss from residential roofs. Emissivity variation among rooftops, however, can lead to a misrepresentation of the temperatures mapped in a thermogram image. The objectives of this study were (a) to demonstrate the feasibility of a technique to use remotely sensed data to calculate surface emissivities and (b) to apply that technique, to determine the extent of emissivity variation in urban surfaces. In the first part of the experimental approach a passive technique is developed to calculate emissivity from two-band thermal infrared radiance data. Inherent limitations and sources of error associated with the technique are discussed. In the second part of the experimental approach the technique was used to measure the emissivity of 1411 roofs within the city limits of Terre Haute, Indiana. Results of this survey indicated that over 98% of the roofs surveyed were confined to a very narrow range of emissivities. It is concluded that the observed variation in rooftop emissivities has a minimal effect on the temperatures depicted in thermograms.     

Diversity and density of urban functions in station areas

The diversity and density of urban functions have been known to affect urban vibrancy positively, but the relation between the two has not been empirically examined; if high density is associated with low diversity in an area, its vibrancy may not increase. To obtain a better understanding of the metabolism of cities and directions for urban planning interventions, this paper offers empirical evidence on the association between the diversity and density of urban functions in the Tokyo Metropolitan Area, using a robust density index that was determined via a Monte Carlo simulation. By conducting association analyses, it was found that highly dense station areas tended to display low diversity at multiple scales. Further investigation indicated that this negative correlation was owing to different spatial characteristics of functions and complementary functioning among highly accessible station areas. This paper argues for considering both diversity and density in urban planning to make station areas vibrant and resilient.     

基于分割注意力机制残差网络的城市区域客流量预测

客流量预测是城市交通资源和公共安全智能化管理的重要依据。为了综合考虑城市乘客人群流动自身的既有周期性、趋势性和突发性,以及与城市物理和社会空间的耦合关系,在时空残差网络的基础上,本文提出了基于深度时空数据的分割注意力机制残差网络的城市细粒度客流量预测模型。首先以不同时空间隔的区域客流量历史数据为基础,引入分割注意力机制模块,为各模态的数据分配不同的权重,动态捕捉更高相关性的抽象数据特征;在时空数据的基础上,引入城市功能区属性作为联合特征,结合节假日、气候等外部特征,形成deep&wide网络结构,有效记忆重要特征对客流量变化的贡献。基于北京出租车数据的区域客流量对比实验表明,相比于传统的深度时空残差网络和其他经典机器学习模型,引入了分割注意力机制和城市功能区特征的预测模型能够更好地提取数据多元化的特征,预测精度明显优于其他同类别方法。     

数据仓库在城市公共建筑能耗管理中的应用

当前众多城市公共建筑能耗监测系统中已收集了大量的建筑能耗数据。针对这些数据源存在的各自独立而且分散,不能够提供全局的数据分析环境,不能够有效支持建筑能耗的评估与建筑节能的研究等问题,提出将数据仓库技术应用于城市公共建筑能耗监管系统的解决方法。通过对建筑能耗监测系统的研究,以及对建筑能耗管理的应用需求的调研,建立城市级公共建筑能耗数据仓库的多维数据模型,对主题设计、指标设计和维度模型设计进行了探讨,并在实验阶段已成功构建了某高校公共建筑能耗数据仓库的实例。实验结果表明,该方法能够有效地为建筑能耗的管理与决策提供良好的数据分析环境。