GEOCAB Portal: A gateway for discovering and accessing capacity building resources in Earth Observation

The discovery of and access to capacity building resources are often essential to conduct environmental projects based on Earth Observation (EO) resources, whether they are Earth Observation products, methodological tools, techniques, organizations that impart training in these techniques or even projects that have shown practical achievements. Recognizing this opportunity and need, the European Commission through two FP7 projects jointly with the Group on Earth Observations (GEO) teamed up with the Committee on Earth observation Satellites (CEOS). The Global Earth Observation CApacity Building (GEOCAB) portal aims at compiling all current capacity building efforts on the use of EO data for societal benefits into an easily updateable and user-friendly portal. GEOCAB offers a faceted search to improve user discovery experience with a fully interactive world map with all inventoried projects and activities. This paper focuses on the conceptual framework used to implement the underlying platform. An ISO19115 metadata model associated with a terminological repository are the core elements that provide a semantic search application and an interoperable discovery service. The organization and the contribution of different user communities to ensure the management and the update of the content of GEOCAB are addressed.     

图割能量驱动的高分辨率遥感影像震害损毁建筑物检测

为充分利用高分辨率遥感影像提供的细节信息,提高震害损毁建筑物检测精度,提出了一种图割框架下融合形状、边缘、角点等多种类型特征的损毁建筑物检测方法。该方法首先利用震前建筑物线划图提取包含单个建筑物的局部影像,用于图割能量函数建模,并分别根据建筑物的位置、形状、边缘以及角点构造能量函数的各约束项。在此基础上,通过最大流/最小割算法求解能量函数最小值,依据最小割能量进行地震前后局部影像中建筑物的相似性度量。最后利用最大期望算法(expectation maximization,EM)求得最小割能量的分类阈值,并根据错分率估值执行后处理以获得最终变化检测结果。采用2011年3月11日东日本大地震前后石卷港的高分辨率遥感影像进行试验,结果表明本文提出的方法能有效检测出损毁建筑物。     

一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法

从LiDAR数据中高精度地提取建筑物屋顶面是构建屋顶面拓扑关系、实现建筑物三维模型重建的关键。本文针对现有算法提取复杂建筑物屋顶面适应性较差、精度较低等问题,提出了一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法。通过主成分分析计算点云特征,构建特征直方图,选取可靠种子点;利用提出的局部点云法向量分布密度聚类算法聚类种子点,快速准确地提取初始屋顶面片;构建基于邻域信息的投票模型,有效地解决屋顶面竞争现象。试验结果表明,本文方法可自动、高精度地提取屋顶面,对不同复杂程度的建筑物具有较好的适应性,能为建筑物三维模型重建提供可靠的屋顶面信息。     

采动区旧址文物类建筑物评价方法

针对采动区旧址文物类建(构)筑物的特殊性,文章提出采动影响下旧址文物类建(构)筑物损坏的多因素单一综合评价方法。以安源煤矿革命旧址建(构)筑物为研究对象,系统分析了革命旧址建(构)筑物的损坏原因,并确定相应的评价指标,采用加权平均法对建(构)筑物的损害进行综合评价分析。结果表明,评价结果与实际损坏程度相符,反映了革命旧址建(构)筑物不同于普通结构建筑物的特殊性,为采动区旧址文物类建(构)筑物损坏的评价方法提供新的途径。     

An Extended Minimum Spanning Tree method for characterizing local urban patterns

Detailed and precise information on urban building patterns is essential for urban design, landscape evaluation, social analyses and urban environmental studies. Although a broad range of studies on the extraction of urban building patterns has been conducted, few studies simultaneously considered the spatial proximity relations and morphological properties at a building-unit level. In this study, we present a simple and novel graph-theoretic approach, Extended Minimum Spanning Tree (EMST), to describe and characterize local building patterns at building-unit level for large urban areas. Building objects with abundant two-dimensional and three-dimensional building characteristics are first delineated and derived from building footprint data and high-resolution Light Detection and Ranging data. Then, we propose the EMST approach to represent and describe both the spatial proximity relations and building characteristics. Furthermore, the EMST groups the building objects into different locally connected subsets by applying the Gestalt theory-based graph partition method. Based on the graph partition results, our EMST method then assesses the characteristics of each building to discover local patterns by employing the spatial autocorrelation analysis and homogeneity index. We apply the proposed method to the Staten Island in New York City and successfully extracted and differentiated various local building patterns in the study area. The results demonstrate that the EMST is an effective data structure for understanding local building patterns from both geographic and perceptual perspectives. Our method holds great potential for identifying local urban patterns and provides comprehensive and essential information for urban planning and management.     

Exploring the relationship between density and completeness of urban building data in OpenStreetMap for quality estimation

OpenStreetMap (OSM) is a free spatial data source based on crowd sourced data. Although the OSM data have a range of applications, such as generating 3D models, and routing and navigation, quality issues are still significant concerns when using the data. Several studies have undertaken quality assessments by comparing OSM data with reference data. However, reference data are not always available due to high costs or licensing restrictions, and very few studies have quantitatively estimated the quality of OSM data under conditions where the corresponding reference data are not available. This study proposed the use of a building density (or building coverage ratio) indicator as a proxy, and designed a series of experiments involving different study areas to quantitatively explore the relationship between building density and building completeness for OSM data in urban areas. The residuals (estimated building completeness and reference building completeness) were also analyzed. Two main results were found from the experiments. (1) There was an approximate linear relationship between building density and building completeness in the OSM data. More precisely, the building completeness of OSM data was approximately 3.4–4 times the building density of OSM data. (2) Approximately 70–80% of the absolute residuals were smaller than 10%, and 80–90% of them were smaller than 20%. This shows that, in most cases, estimated building completeness was close to the corresponding reference building completeness. Therefore, we concluded that the building density indicator is a potential proxy for the quantitative completeness estimation of OSM building data in urban areas. The limitations of using this indicator were also addressed.     

Performance of shape indices and classification schemes for characterising perceptual shape complexity of building footprints in GIS

Shape characterisation is important in many fields dealing with spatial data. For this purpose, numerous shape analysis and recognition methods with different degrees of complexity have so far been developed. Among them, relatively simple indices are widely used in spatial applications, but their performance has not been investigated sufficiently, particularly for building footprints (BFs). Therefore, this article focuses on BF shape characterisation with shape indices and classification schemes in a GIS environment. This study consists of four phases. In the first phase, the criteria for BF shape complexity were identified, and accordingly, benchmark data was constructed by human experts in three shape complexity categories. In the second phase, 18 shape indices were selected from the literature and automatically computed in GIS. The performance of these indices was then statistically assessed with histograms, correlation matrix and boxplots, and consequently four indices were found to be appropriate for further investigation. In the third phase, two new indices (Equivalent Rectangular index and Roughness index) were proposed with the objective to measure some BF shape characteristics more efficiently. The proposed indices also were found to be appropriate with the same statistical assessment procedures. In the final phase, BF shape complexity categories were created with the pairs of six appropriate indices and four choropleth mapping classification schemes (equal intervals, natural break, standard deviation, and custom) in GIS. The performance of the index–scheme pairs was assessed against the benchmark data. The findings demonstrated that both new indices and two of the selected indices (Convexity and Rectangularity) delivered higher performance. The custom classification scheme was found more ideal to reveal absolute shape complexity with the index value ranges derived from the boxplots while the other classification schemes were more appropriate to reveal relative shape complexity.     

基于分类与形态综合的高分辨率影像建筑物提取方法研究

本文提出了一种基于分类和形态综合的高分辨率影像建筑物提取方法。主要研究的是基于高分辨率影像多尺度分割的面向对象人工地物分类方法,并以此为基础提取房屋,实现房屋位置、范围的初步自动化提取。最后,对提取的地物图斑的精确边界利用Hough变换提取直线边缘轮廓线,并以此为基础进行轮廓拟合和制图综合,从而得到规则的、方形组合的建筑物形状信息。     

分数阶傅里叶变换联合支持向量机的建筑物变形预测

提出一种分数阶傅里叶变换(fractional Fourier transformation, FrFT)与支持向量机(support vector machine, SVM)相结合的建筑物变形组合预测模型。首先利用FrFT对变形时间序列进行多尺度分析,将复杂时间序列分解为一系列结构较为简单的子序列;然后利用SVM对每个子序列分别建立预测模型,通过将各个子序列的预测结果进行综合叠加,得到最终预测结果;同时考虑到SVM模型参数选择的难题,提出一种改进果蝇优化算法(improved fruit fly optimization algorithm, IFOA)对其进行全局寻优,提升预测性能。以西南地区某混凝土坝变形实测数据为例开展验证实验,结果表明,本文组合预测模型能够充分挖掘数据中隐含的趋势性和规律性信息,获得较高的预测精度。