三维虚拟城市自动化生成方法研究

从城市元胞自动机模型的角度研究三维虚拟城市的自动化生成方法.通过建立城市元胞自动机模型,模拟城市土地单元的演化过程,根据演化结果确定各土地单元的空间位置和相应的建筑物类型.研究利用Creator进行三维场景建模,利用OpenGVS场景管理技术随机生成建筑物,并将其布设在三维场景的相应位置上,生成三维虚拟城市,并就三维虚拟城市自动化生成系统的功能结构设计和数据结构、空间坐标转换等技术问题进行了探讨.     

Adaptive multi-resolution labeling in virtual landscapes

Labeling plays an important role in map production, attaching specific texts to related geographic elements to provide clear environmental references. In three-dimensional geographical information systems (3DGISs), however, cluttering happens fairly commonly because of the unexpected overlapping and occlusion among labels and related objects, and results in an ambiguous and obscure environment. It generally also takes large computing power and memory to visualize spatial entities. Aimed at both unambiguous and efficient 3D map display, this article proposes an adaptive multi-resolution labeling method to deal with point, polyline, and polygon features labeling in a 3D landscape. It implements adaptive placement and view-driven label filtering without obscuring other visual features. The experiments indicated that the display of overlapping labels and label popping are reduced significantly with less computation burden while retaining the rendering quality.     

Extending a BIM-based data model to support 3D digital management of complex ownership spaces

Currently, 2D-based analogue building subdivision plans are used to represent the spatial extent of private, public and communal property ownership rights. These plans are recognized as posing a range of challenges in terms of communicating the spatial complexity of ownership spaces within multi-storey buildings. In response to these challenges, three-dimensional (3D) digital data environments are being investigated as a potential approach for managing complex, vertically stratified ownership arrangements. The argument presented in this article is that Building Information Modelling (BIM) can be adopted for 3D digital management of data related to complex ownership spaces. BIM provides a common and 3D digital data sharing space, underpinning a reliable basis for facilitating collaboration and decision-making over the lifecycle of buildings. However, ownership attributes and the spatial structure of ownership arrangements inside buildings are yet to be accommodated within the BIM data environment. In this article, we present a range of required data elements for managing complex ownership spaces, which have been elicited by investigating current practices pertaining to subdivision of ownership spaces within multi-storey buildings in Melbourne, Australia. An open data model in the BIM domain is extended with these data elements and a prototype model for a real multi-storey building is implemented to demonstrate the viability of the extended data model for 3D digital management and visualization of data related to complex ownership arrangements.     

Quality assessment for building footprints data on OpenStreetMap

In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data.     

Automatic extraction of building statistics from digital orthophotos

In this paper we discuss uses of image segmentation, feature extraction and Bayesian networks for identifying buildings in digital orthophotos and the utilisation of the results for the automated computation of building statistics. Our work differs from previous attempts in a number of ways. Firstly, image segmentation is accomplished using an adaptive multi-scale method which brings together region and edge information to segment the image into regions. Secondly, automated building feature extraction (e.g. corners) is optimised to fit with expert human annotation performance. The third aspect of this work is the exploration of Bayesian networks as a method for fusing available information (ranging from corner information to solar angles as indicators of shadow location) to classify segmented regions as corresponding to buildings or not. Such processes then permit the automatic compilation of building statistics.     

A three-step approach of simplifying 3D buildings modeled by CityGML

CityGML (City Geography Markup Language), the OGC (Open Open Geospatial Consortium) standard on three-dimensional (3D) city modeling, is widely used in an increasing number of applications, because it models a city with rich geometrical and semantic information. The underlying building model differentiates four consecutive levels of detail (LoDs). Nowadays, most city buildings are reconstructed in LoD3, while few landmarks in LoD4. For visualization or other purposes, buildings in LoD2 or LoD1 need to be derived from LoD3 models. But CityGML does not indicate methods for the automatic derivation of the different LoDs. This article presents an approach for deriving LoD2 buildings from LoD3 models which are essentially the exterior shells of buildings without opening objects. This approach treats different semantic components of a building separately with the aim to preserve the characteristics of ground plan, roof, and wall structures as far as possible. The process is composed of three steps: simplifying wall elements, generalizing roof structures, and then reconstructing the 3D building by intersecting the wall and roof polygons. The first step simplifies ground plan with wall elements projected onto the ground. A new algorithm is developed to handle not only simple structures like parallel and rectangle shapes but also complicated structures such as non-parallel, non-rectangular shapes and long narrow angles. The algorithm for generalizing roof structure is based on the same principles; however, the calculation has to be conducted in 3D space. Moreover, the simplified polygons of roof structure are further merged and typified depending on the spatial relations between two neighboring polygons. In the third step, generalized 3D buildings are reconstructed by increasing walls in height and intersecting with roof structures. The approach has been implemented and tested on a number of 3D buildings. The experiments have verified that the 3D building can be efficiently generalized, while the characteristics of wall and roof structure can be well preserved after the simplification.     

基于改进DeepLabv3plus算法的遥感图像海岛建筑提取方法

目前海岛经济快速发展,为避免海岛建筑无序扩建,了解海岛建筑分布特征尤为重要。机器学习方法是从高分遥感影像提取地物目标的常见方式,然而建筑物遥感特征复杂,机器学习方法出现鲁棒性差、难以充分挖掘深层次特征的弊端。文章提出基于DeepLabv3plus网络模型的深度学习语义分割方法提取海岛建筑,并对网络结构进行改进,使用组归一化（GN）方法替代批归一化（BN）以适合小batch size下的语义分割操作。针对海岛建筑数据量较少的问题,采用迁移学习策略,设计基于多源数据的国内城市建筑数据集的预训练样本智能采集和标注方法,再人工标注中国部分海岛建筑进行算法实验。结果表明,在batch size较小时,基于GN的DeepLabv3plus语义分割算法的平均精度和mIoU均得到提升,能够获得更为精确的像素级海岛建筑提取结果。     

一种基于机载LiDAR和离散曲率的建筑物三维重建方法

机载LiDAR作为一种获取地物空间信息的新技术已得到广泛应用,但从LiDAR数据中重建建筑物三维模型方法的缺乏,是制约其进一步发展的瓶颈.该文介绍了一种自动与人机交互建模相结合的建筑物三维重建方法,首先基于离散曲率分析自动提取建筑物3D轮廓信息,然后将建筑物轮廓信息作为约束条件,使用模型驱动的ATOP算法实现建筑物自动建模,并支持对复杂建筑物人机交互建模.     

基于手机信令数据的城市居民非通勤出行群体画像——以苏州市为例

构建考虑多维特征的城市居民非通勤出行群体画像概念模型,提出一种按序结合相关系数矩阵与二阶聚类的方法,以进行非通勤出行群体画像。利用苏州市手机信令数据,基于非通勤出行时空规律和社会属性将城市居民出行者进行群体划分,并结合城市居民非通勤出行群体画像概念模型对不同类型非通勤出行群体进行多维度解析。结果显示：① 城市居民出行者可划分为：活跃?波动?工作日主导型群体、非活跃?稳定?均衡型群体。② 不同类型非通勤出行群体画像在多维特征上存在显著差异。③ 根据群体画像标签关联分析,除显性关联外,群体画像不同标签间存在隐性关联。     

高密度城区建筑图斑智能识别与屋顶绿化适建性评估——以成都市为例（英文）

随着城市扩张,城市绿地被占用,城市热岛效应严重,对城市建筑屋顶面进行绿化,是增加城市绿地面积、改善城区生态环境的有效途径。针对高密度城区建设用地趋于饱和及生态环境难以拓展的现状,本文利用高分二号卫星影像,通过深度学习辅助人工修正获取研究区精确建筑图斑,建立含屋顶自身属性特征、自然环境特征、人文社会环境特征的屋顶绿化评价指标体系,运用层次分析法计算非屋顶自身属性特征的权重值,通过空间连接、加权叠加等空间分析方法综合评估适宜绿化的屋顶面。文章以成都市三环线以内区域为示范区展开研究,研究结果表明,通过深度学习获取的精确的建筑图斑,可以提高实验效率;而通过特征提取的方法,可对不适宜绿化的屋顶面有效地进行分类,精度达86.58%,其中适宜绿化的图斑数量占总数比48.08%,高适类、中适类、低适类的建筑图斑占比分别为45.32%、38.95%、15.73%,高适宜绿化建筑主要分布在成都市一环区及一环区以外西部地区。研究结果可为后续空间规划研究提供有效数据支撑。     

A spatial cognition-based urban building clustering approach and its applications

This article presents a spatial cognition analysis technique for automated urban building clustering based on urban morphology and Gestalt theory. The proximity graph is selected to present the urban mrphology. The proximity graph considers the local adjacency among buildings, providing a large degree of freedom in object displacement and aggregation. Then, three principles of Gestalt theories, proximity, similarity, and common directions, are considered to extract potential Gestalt building clusters. Next, the Gestalt features are further characterized with seven indicators, that is, area difference, height difference, similarity difference, orientation difference, linear arrangement difference, interval difference, and oblique degree of arrangement. A support vector machine (SVM)-based approach is employed to extract the Gestalt building clusters. This approach transforms the Gestalt cluster extraction into a supervised discrimination process. The method presents a generalized approach for clustering buildings of a given street block into groups, while maintaining the spatial pattern and adjacency of buildings during the displacement operation. In applications of urban building generalization and three-dimensional (3D) urban panoramic-like view, the method presented in this article adequately preserves the spatial patterns, distributions, and arrangements of urban buildings. Moreover, the final 3D panoramic-like views ensure the accurate appearance of important features and landscapes.     

A node semantic similarity schema-matching method for multi-version Web Coverage Service retrieval

Different versions of the Web Coverage Service (WCS) schemas of the Open Geospatial Consortium (OGC) reflect semantic conflict. When applying the extended FRAG-BASE schema-matching approach (a schema-matching method based on COMA++, including an improved schema decomposition algorithm and schema fragments identification algorithm, which enable COMA++-based support to OGC Web Service schema matching), the average recall of WCS schema matching is only 72%, average precision is only 82% and average overall is only 57%. To improve the quality of multi-version WCS retrieval, we propose a schema-matching method that measures node semantic similarity (NSS). The proposed method is based on WordNet, conjunctive normal form and a vector space model. A hybrid algorithm based on label meanings and annotations is designed to calculate the similarity between label concepts. We translate the semantic relationships between nodes into a propositional formula and verify the validity of this formula to confirm the semantic relationships. The algorithm first computes the label and node concepts and then calculates the conceptual relationship between the labels. Finally, the conceptual relationship between nodes is computed. We then use the NSS method in experiments on different versions of WCS. Results show that the average recall of WCS schema matching is greater than 83%; average precision reaches 92%; and average overall is 67%.     

高层建筑群平面布局类型对室外风环境影响的对比研究

现有城市热环境研究关注于城市尺度范围内城市形态,未充分考虑小尺度区域内、非匀质分布的建筑群布局形态对热环境,尤其是室外风环境的影响。以6幢方形截面高层建筑组成的建筑群的周围风环境为研究对象,采用数值模拟方法进行对比、分析,对比6种布局形式的建筑群的室外人行高度(1.5 m)处的风速比和风向分布,得出风环境优劣状况与平面布局之间的关系,为城市建设、高层建筑布局规划提供参考与评价依据。     

上海城市天际线与高层建筑发展之关系分析

以上海市2000年中心城区航空影像为基础,基于GIS技术分析城市高层建筑与天际线的分布特点及其影响机制。认为城市天际线与高层建筑相互制约,高层建筑的发展与布局必须考虑城市天际线的特色。     

3D building roof reconstruction from airborne LiDAR point clouds: a framework based on a spatial database

Three-dimensional (3D) building models are essential for 3D Geographic Information Systems and play an important role in various urban management applications. Although several light detection and ranging (LiDAR) data-based reconstruction approaches have made significant advances toward the fully automatic generation of 3D building models, the process is still tedious and time-consuming, especially for massive point clouds. This paper introduces a new framework that utilizes a spatial database to achieve high performance via parallel computation for fully automatic 3D building roof reconstruction from airborne LiDAR data. The framework integrates data-driven and model-driven methods to produce building roof models of the primary structure with detailed features. The framework is composed of five major components: (1) a density-based clustering algorithm to segment individual buildings, (2) an improved boundary-tracing algorithm, (3) a hybrid method for segmenting planar patches that selects seed points in parameter space and grows the regions in spatial space, (4) a boundary regularization approach that considers outliers and (5) a method for reconstructing the topological and geometrical information of building roofs using the intersections of planar patches. The entire process is based on a spatial database, which has the following advantages: (a) managing and querying data efficiently, especially for millions of LiDAR points, (b) utilizing the spatial analysis functions provided by the system, reducing tedious and time-consuming computation, and (c) using parallel computing while reconstructing 3D building roof models, improving performance.     

Towards an evaluation of quality for names placement methods

The cartographic labelling problem is the problem of placing text on a map. This includes the positioning of the labels, and determining the shape in the case of line and area feature labels. There are many rules and customs that describe aspects of good label placement, like readability and clear association. This paper gives a classification of most label placement rules, and formalizes them into a function that can serve as a quality measure for label placement. If such a function is implemented, it allows comparison of the output of different label placement programs. We give a simple and a more refined example of the quality function.     

Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry

The international standard CityGML defines five levels of detail (LODs) for 3D city models, but only the highest of these (LOD4) supports modelling the indoor geometry of a building, which must be acquired in correspondingly high detail and therefore at a high cost. Whereas simple 3D city models of the exterior of buildings (e.g. CityGML LOD2) can be generated largely automatically, and are thus now widely available and have a great variety of applications, similarly simple models containing their indoor geometries are rare.

In this paper we present two contributions: (i) the definition of a level of detail LOD2+, which extends the CityGML LOD2 specification with indoor building geometries of comparable complexity to their exterior geometries in LOD2; and more importantly (ii) a method for automatically generating such indoor geometries based on existing CityGML LOD2 exterior geometries. We validate our method by generating LOD2+ models for a subset of the Rotterdam 3D data set and visually comparing these models to their real counterparts in building blueprints and imagery from Google Street View and Bing Maps. Furthermore, we use the LOD2+ models to compute the net internal area of each dwelling and validate our results by comparing these values to the ones registered in official government data sets.     

Landscape metrics for three-dimensional urban building pattern recognition

Understanding how landscape pattern determines population or ecosystem dynamics is crucial for managing our landscapes. Urban areas are becoming increasingly dominant social-ecological systems, so it is important to understand patterns of urbanization. Most studies of urban landscape pattern examine land-use maps in two dimensions because the acquisition of 3-dimensional information is difficult. We used Brista software based on Quickbird images and aerial photos to interpret the height of buildings, thus incorporating a 3-dimensional approach. We estimated the feasibility and accuracy of this approach. A total of 164,345 buildings in the Liaoning central urban agglomeration of China, which included seven cities, were measured. Twelve landscape metrics were proposed or chosen to describe the urban landscape patterns in 2- and 3-dimensional scales. The ecological and social meaning of landscape metrics were analyzed with multiple correlation analysis. The results showed that classification accuracy compared with field surveys was 87.6%, which means this method for interpreting building height was acceptable. The metrics effectively reflected the urban architecture in relation to number of buildings, area, height, 3-D shape and diversity aspects. We were able to describe the urban characteristics of each city with these metrics. The metrics also captured ecological and social meanings. The proposed landscape metrics provided a new method for urban landscape analysis in three dimensions.