高分辨率遥感影像图制作的镶嵌线自动提取

为更好地发挥遥感技术在城市规划地图制作中的应用,高分辨率遥感影像成为城市地图制作中最重要的数据源。面对地物信息复杂、建筑物众多的城市地区,如何快速提取高分辨率遥感影像地图制作过程中相邻两景影像之间的镶嵌线具有重要意义。本文以国产卫星中分辨率最高、幅宽最小的GF-2影像为数据源,融合建筑物轮廓数据,研究了基于最短路径的A*搜索算法,实现了遥感影像地图制作的镶嵌线自动提取技术。结果表明,该方法能够自动生成避让建筑物的镶嵌线,速度快、镶嵌质量高,可广泛应用于城市地区高分辨率遥感影像地图制作。     

Automatic near-photorealistic 3-D modelling and texture mapping for rectilinear buildings

Three-dimensional (3-D) representations of urban regions have gained much attention because of recent developments in remote sensing and computer graphics technologies. In particular, textured 3-D building reconstruction for a variety of applications has been a popular research topic in recent years. In this study, we present the reconstruction of 3-D building models along with texture selection and mapping. Extracted two-dimensional building patches and normalized digital surface model (nDSM) data are used to generate the 3-D models. To build near-photorealistic 3-D models, the acquired geo-referenced facade textures are associated with the corresponding building facades using an automated GPS-assisted approach. On the other hand, the modelling and texture mapping of the roof structures were carried out manually. The study area is composed of eight housing estates (blocks), where a total of 110 buildings were analysed. The whole study area was modelled, with facade textures, in less than 1 min of processor running time with an acceptable level of accuracy. The texture mapping was carried out using MATLAB’s Virtual Reality Toolbox?.     

Evaluation of LiDAR and image segmentation based classification techniques for automatic building footprint extraction for a segment of Atlantic County,New Jersey

Extracting high-quality building footprints is a basic requirement in multiple sectors of town planning, disaster management, 3D visualization, etc. In the current study, we compare three different techniques for acquiring building footprints using (i) LiDAR, (ii) object-oriented classification (OOC) applied on high-resolution aerial photographs and (iii) digital surface models generated from interpolated LiDAR point cloud data. The three outputs were compared with a digitized sample of building polygons quantitatively by computing the errors of commission and omission, and qualitatively using statistical operations. These findings showed that building footprints derived from OOC gave highest regression and correlation values with least commission error. The R² and R values (0.86 and 0.92, respectively) imply that the footprint areas derived by OOC matched more closely with the actual area of buildings, while a low commission error of 24.7% represented a higher number of footprints as correctly classified.     

Estimating Geographical PV Potential Using LiDAR Data for Buildings in Downtown San Francisco

Sustainable solar energy is of the interest for the city of San Francisco to meet their renewable energy initiative. Buildings in the downtown area are expected to have great photovoltaic (PV) potential for future solar panel installation. This study presents a comprehensive method for estimating geographical PV potential using remote sensed LiDAR data for buildings in downtown San Francisco. LiDAR derived DSMs and DTMs were able to generate high quality building footprints using the object‐oriented classification method. The GRASS built‐in solar irradiation model (r.sun) was used to simulate and compute PV yields. Monthly and yearly maps, as well as an exquisite 3D city building model, were created to visualize the variability of solar irradiation across the study area. Results showed that monthly sum of solar irradiation followed a one‐year cycle with the peak in July and troughs in January and December. The mean yearly sum of solar irradiation for the buildings in the study area was estimated to be 1675 kWh/m². A multiple regression model was used to test the significance of building height, roof area and roof complexity against PV potential. Roof complexity was found to be the dominant determinant. Uncertainties of the research are mainly from the inherent r.sun limitations, boundary problems, and the LiDAR data accuracy in terms of both building footprint extraction and 3D modeling. Future work can focus on a more automated process and segment rooftops of buildings to achieve more accurate estimation of PV potential. The outcome of this research can assist decision makers in San Francisco to visualize building PV potential, and further select ideal places to install PV systems. The methodology presented and tested in this research can also be generalized to other cities in order to meet contemporary society's need for renewable energy.     

基于移动激光扫描点云数据和遥感图像的建筑物三维模型快速建模方法

建筑物的三维建模是城市三维建模和可视化的重要组成部分。本文提出一种基于点云数据与遥感图像的建筑物三维模型快速建模方法。首先,运用改进的RANSAC法从点云数据中提取建筑立面,根据立面区分平顶建筑与人字形屋顶建筑;在此基础上,进一步对建筑物的高度进行提取;之后,利用区域增长法从遥感图像中提取建筑物屋顶轮廓,利用形态学方法对提取出的轮廓进行规则化处理,并基于Freeman链码提取轮廓角点,得到规整的轮廓;最后,根据提取出的建筑高度属性对屋顶轮廓拉伸并进行纹理映射,实现对建筑物的三维重建。通过实例证明,提出的方法能快速、高效地实现建筑物三维模型的重建。     

Object based building footprint detection from high resolution multispectral satellite image using K-means clustering algorithm and shape parameters

Object-based image analysis (OBIA) has been a new area of research in satellite image processing applications, since it improves the quality of information acquisition about geospatial objects and also enables to add spatial and contextual information to the objects of interest. The extraction of buildings from High Resolution Satellite (HRS) image in an urban scenario has been an intricate problem due to their different size, shape, varying rooftop textures and low contrast between building and surrounding region. In this study, a new object-based automatic building extraction technique has been proposed to extract building footprints from HRS pan sharpened IKONOS multispectral image. The study is mainly emphasizing on obtaining optimal values for segmentation parameters, shape parameters, and defining rule set to extract buildings and eliminate misclassified other urban features. The suitability of the technique has been judged using different indicators, such as, completeness, correctness and quality.     

一种含室内空间结构的建筑物3D LOD概念模型与生成方法

针对现有建筑物多细节层次（level of detail,LOD）表达主要面向外部,很少涉及内部空间的问题,提出一种含室内空间结构的建筑物三维LOD概念模型。该模型分为LOD0~LOD6 7个LOD,细节层次由建筑物的基底平面图、幢模型、无侧面楼层模型、楼层模型、户模型、房间模型到含门、窗等建筑语义的信息房间模型,空间细节逐次变细,丰富了建筑物室内空间LOD的表达理论。为构建这种LOD模型,提出基于胞腔复形链边界算子和上边界算子的LOD生成方法。该方法引入代数拓扑的胞腔、胞腔复形和复形链3个概念,利用胞腔和胞腔复形表达LOD,并在LOD6的基础上,利用边界算子和上边界算子生成其他粗空间粒度LOD模型,为三维城市模型建筑物LOD构建提供一种新方法。     

A generative statistical approach to automatic 3D building roof reconstruction from laser scanning data

This paper presents a generative statistical approach to automatic 3D building roof reconstruction from airborne laser scanning point clouds. In previous works, bottom-up methods, e.g., points clustering, plane detection, and contour extraction, are widely used. Due to the data artefacts caused by tree clutter, reflection from windows, water features, etc., the bottom-up reconstruction in urban areas may suffer from a number of incomplete or irregular roof parts. Manually given geometric constraints are usually needed to ensure plausible results. In this work we propose an automatic process with emphasis on top-down approaches. The input point cloud is firstly pre-segmented into subzones containing a limited number of buildings to reduce the computational complexity for large urban scenes. For the building extraction and reconstruction in the subzones we propose a pure top-down statistical scheme, in which the bottom-up efforts or additional data like building footprints are no more required. Based on a predefined primitive library we conduct a generative modeling to reconstruct roof models that fit the data. Primitives are assembled into an entire roof with given rules of combination and merging. Overlaps of primitives are allowed in the assembly. The selection of roof primitives, as well as the sampling of their parameters, is driven by a variant of Markov Chain Monte Carlo technique with specified jump mechanism. Experiments are performed on data-sets of different building types (from simple houses, high-rise buildings to combined building groups) and resolutions. The results show robustness despite the data artefacts mentioned above and plausibility in reconstruction.     

旧城改造中建筑物立面快速提取方法

旧城改造目前面临诸多问题,如传统的测量手段不仅测量效率低,而且对于结构比较复杂的建筑物无法精确地获取建筑物立面的全部信息,施测不全面,建模效率低等。首先利用三维激光扫描技术获取建筑物精细、整体的点云数据,借助多个建模平台联合作业的方式,结合现场照片进行复杂建筑物的三维重建,从而获取目标建筑物各个方向的立面信息。经过大量验证和对比分析,该方法可以快速获得建筑物精确、真实、全面的立面信息,提高工作效率,为智慧城市的建设和规划提供详细可靠的信息支持。     

车载移动测量系统在建筑物立面改造中的应用

随着城市现代化建设的深入,临街建筑物的立面改造成为当今城市化建设面临的重大课题。本文结合合肥市临街建筑物立面改造工作,阐述了以车载移动测量系统为数据采集设备进行建筑物外立面成图的方法,为立面改造提供了基础数据。以工程实例说明利用车载移动测量系统进行建筑物立面采集工作能够明显地提高工作效率,为移动测量系统在建筑物立面改造中的应用提供参考。     

Roof model recommendation for complex buildings based on combination rules and symmetry features in footprints

Currently, very few roof shape information for complex buildings is available on OSM. Moreover, additional data requirements (e.g. 3D point clouds) limit the applicability of many roof reconstruction approaches. To mitigate this issue, we propose an approach to roof shape recommendations for complex buildings by exploring the inherited characteristics of building footprints: the disclosure of rectangles combinations in a partition of footprints and the symmetrical features of footprints. First, it decomposes a complex footprint into rectangles by using an advanced minimal non-overlapping cover algorithm. Second, a graph-based symmetry detection algorithm is proposed to identify all the symmetrical sub-clusters in partitions. Then, a set of selection rules are defined to rank partitions, and the best ones are chosen for roof shape recommendation. Finally, a set of combination rules and a symmetry rule are defined. It enables to evaluate the probability of a footprint being a certain combination of roof shapes. Experimental results show the growth of the probability of correctly recommending roof shapes for single rectangles and buildings from a prior probability of 17–45% and from a prior probability of 0.29–14.3%, removing 60% and 93% of the incorrect roof shape options, respectively.     

Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection

The urban land cover mapping and automated extraction of building boundaries is a crucial step in generating three-dimensional city models. This study proposes an object-based point cloud labelling technique to semantically label light detection and ranging (LiDAR) data captured over an urban scene. Spectral data from multispectral images are also used to complement the geometrical information from LiDAR data. Initial object primitives are created using a modified colour-based region growing technique. Multiple classifier system is then applied on the features extracted from the segments for classification and also for reducing the subjectivity involved in the selection of classifier and improving the precision of the results. The proposed methodology produces two outputs: (i) urban land cover classes and (ii) buildings masks which are further reconstructed and vectorized into three-dimensional buildings footprints. Experiments carried out on three airborne LiDAR datasets show that the proposed technique successfully discriminates urban land covers and detect urban buildings.     

顾及格式塔原则的建筑物群移位实数编码遗传算法

在建筑物群的综合过程中,建筑物群的移位是一个非常重要的任务。在分析已有算法的基础上,探讨了建筑物群移位的一个新的自适应实数编码遗传算法,在该算法中,考虑了建筑物与街道之间的空间关系和建筑物群排列在图形表达上的格式塔原则,在适应度函数的设计上也考虑了多重因素的影响,使得移位结果更符合地图制图规范和地图感受规律。实验结果表明,该算法在移位空间充足时能精确收敛到最优解,移位效果良好。     

Building population mapping with aerial imagery and GIS data

Geospatial distribution of population at a scale of individual buildings is needed for analysis of people's interaction with their local socio-economic and physical environments. High resolution aerial images are capable of capturing urban complexities and considered as a potential source for mapping urban features at this fine scale. This paper studies population mapping for individual buildings by using aerial imagery and other geographic data. Building footprints and heights are first determined from aerial images, digital terrain and surface models. City zoning maps allow the classification of the buildings as residential and non-residential. The use of additional ancillary geographic data further filters residential utility buildings out of the residential area and identifies houses and apartments. In the final step, census block population, which is publicly available from the U.S. Census, is disaggregated and mapped to individual residential buildings. This paper proposes a modified building population mapping model that takes into account the effects of different types of residential buildings. Detailed steps are described that lead to the identification of residential buildings from imagery and other GIS data layers. Estimated building populations are evaluated per census block with reference to the known census records. This paper presents and evaluates the results of building population mapping in areas of West Lafayette, Lafayette, and Wea Township, all in the state of Indiana, USA.     

A Technique for Building Representation in Oblique View Maps of Modern Urban Areas

Abstract

This paper presents a technique for creating oblique view maps of urban areas. We identify and apply cartographic and cognitive principles to develop a solution in the context of state-of-art geographic information systems. The gap in the ability of these systems to render three-dimensional buildings into maps is addressed. At the core of our solution is a building facade modelling approach that supports varying degrees of abstraction. This is achieved by introducing a concept of 'facade waveforms' and representing building facades as combinations of these waveforms. A Fourier series approximation of the waveforms is used during the rendering processes resulting in an elegant solution to anti-aliasing. The formulation retains the semantic information in the representation that enables meaningful extensions like night time facade generation. The solution is implemented as a pixel shader and therefore leads to a large reduction in texture memory requirement compared with existing building rendering techniques. Additionally, in the case of web based systems, there is significant reduction in bandwidth requirement. We highlight the features of the proposed solution by generating demonstrative maps and images.     

An efficient stochastic approach for building footprint extraction from digital elevation models

In the past two decades, building detection and reconstruction from remotely sensed data has been an active research topic in the photogrammetric and remote sensing communities. Recently, effective high level approaches have been developed, i.e., the ones involving the minimization of an energetic formulation. Yet, their efficiency has to be balanced by the amount of processing power required to obtain good results.In this paper, we introduce an original energetic model for building footprint extraction from high resolution digital elevation models (≤1 m) in urban areas. Our goal is to formulate the energy in an efficient way, easy to parametrize and fast to compute, in order to get an effective process still providing good results.Our work is based on stochastic geometry, and in particular on marked point processes of rectangles. We therefore try to obtain a reliable object configuration described by a collection of rectangular building footprints. To do so, an energy function made up of two terms is defined: the first term measures the adequacy of the objects with respect to the data and the second one has the ability to favour or penalize some footprint configurations based on prior knowledge (alignment, overlapping, …). To minimize the global energy, we use a Reversible Jump Monte Carlo Markov Chain (RJMCMC) sampler coupled with a simulated annealing algorithm, leading to an optimal configuration of objects. Various results from different areas and resolutions are presented and evaluated. Our work is also compared with an already existing methodology based on the same mathematical framework that uses a much more complex energy function. We show how we obtain similarly good results with a high computational efficiency (between 50 and 100 times faster) using a simplified energy that requires a single data-independent parameter, compared to more than 20 inter-related and hard-to-tune parameters.     

利用地理标签数据感知城市活力

高度活跃的城市是社会稳定发展的基础。基于地理标签感知的城市活力能够量化城市发展现状,探索城市活力的影响机制,为精细化城市治理提供技术支撑。传统城市活力研究依赖于街区的活力调查,时间长,费用高。本文研究利用兴趣点和社交媒体签到等地理标签数据,提出了城市活力度量指标,探索性分析城市活力的分布模式。基于土地利用、道路和建筑物等数据计算建成环境指标,构建城市活力和建成环境之间的普通线性回归与空间自回归模型,揭示了影响城市活力的建成环境因素。基于深圳市的试验结果表明:兴趣点和社交媒体签到数据能够较好地指示城市活力。深圳市的城市活力主要受商业用地、工业用地、土地混合利用以及路网密度、地铁站点密度的影响。住宅用地和建筑物占地密度对基于POI的城市活力具有显著影响。     

Building segmentation and outline extraction from UAV image-derived point clouds by a line growing algorithm

ABSTRACT

This paper presents an approach to process raw unmanned aircraft vehicle (UAV) image-derived point clouds for automatically detecting, segmenting and regularizing buildings of complex urban landscapes. For regularizing, we mean the extraction of the building footprints with precise position and details. In the first step, vegetation points were extracted using a support vector machine (SVM) classifier based on vegetation indexes calculated from color information, then the traditional hierarchical stripping classification method was applied to classify and segment individual buildings. In the second step, we first determined the building boundary points with a modified convex hull algorithm. Then, we further segmented these points such that each point was assigned to a fitting line using a line growing algorithm. Then, two mutually perpendicular directions of each individual building were determined through a W-k-means clustering algorithm which used the slop information and principal direction constraints. Eventually, the building edges were regularized to form the final building footprints. Qualitative and quantitative measures were used to evaluate the performance of the proposed approach by comparing the digitized results from ortho images.     

车载联合机载LiDAR点云数据的建筑物立面精细分割

从数据量庞大且散乱的车载LiDAR点云中分割出建筑物立面数据是一项繁琐而艰巨的工作。本文提出一种结合机载LiDAR点云的车载LiDAR点云建筑物立面分割方法。该方法在空-地点云严格配准的基础上,从机载LiDAR点云中分割出每栋建筑物的顶部点云,提取建筑物顶部外轮廓线并进行规则矢量化处理,设置轮廓线缓冲区实现立面点云的粗分割;再采用基于稳健特征值的平面拟合法对单栋建筑物的每个立面进行去噪滤波,实现建筑物立面的精细分割。试验结果证明了该算法对城市场景中车载LiDAR点云处理的有效性。     

一种建筑物只能聚类方法

建筑物聚类是大比例尺地图自动制图综合中需要解决的关键问题。通过分析Gestalt原理的邻近性、相似性等,采用建筑物重心、建筑物间的距离、建筑物与邻近线状地物要素间位置关系等参数描述建筑物。本文提出的建筑物智能聚类方法包含两个连续的步骤:首先计算建筑物的描述参数,利用SOM网络的聚类能力,进行建筑物的初步聚类;然后,利用SOM竞争层行列扫描的方法,对初步聚类的建筑物类簇进行精确划分,获得满足建筑物聚类的全局和局部约束条件等制图要求的建筑物聚类群组。