Geometric modeling of geospatial data for visualization-assisted excavation

Underground utility lines being struck by mechanized excavators during construction or maintenance operations is a long standing problem. Besides the disruptions to public services, daily life, and commerce, utility strike accidents lead to injuries, fatalities, and property damages that cause significant financial loss. Utility strikes by excavation occur mainly because of the lack of an effective approach to synergize the geospatial utility locations and the movement of excavation equipment into a real-time, three-dimensional (3D) spatial context that is accessible to excavator operators. A critical aspect of enabling such a knowledge-based excavation approach is the geospatial utility data and its geometric modeling. Inaccurate and/or incomplete utility location information could lead to false instilled confidence and be counterproductive to the excavator operator. This paper addresses the computational details in geometric modeling of geospatial utility data for 3D visualization and proximity monitoring to support knowledge-based excavation. The details of the various stages in the life-cycle of underground utility geospatial data are described, and the inherent limitations that preclude the effective use of the data in downstream engineering applications such as excavation guidance are analyzed. Five key requirements - Interactivity, Information Richness, 3-Dimensionality, Accuracy Characterization, and Extensibility – are identified as necessary for the consumption of geospatial utility data in location-sensitive engineering applications. A visualization framework named IDEAL that meets the outlined requirements is developed and presented in this paper to geometrically represent buried utility geospatial data and the movement of excavation equipment in a 3D emulated environment in real-time.     

The utility of built environment geospatial data for high-resolution dasymetric global population modeling

Several global gridded population data sets are available at unprecedented high-resolution, including recent releases at 100-m, 30-m, and 10-m resolution. These data sets are the result of the application of advanced methods to disaggregate census population counts from administrative units and facilitated by the proliferation of increasingly high-resolution spatial information pertaining to the built environment (e.g. built-up and building footprint data). Accordingly, these gridded population data are increasingly dependent on a single ancillary data set to inform the distribution of populations across space. Our study tests several combinations of binary masking variables (land areas, all building footprints, residential building footprints) and density variables (building footprint areas, building volumes) derived from characteristics of the built environment at 20× and 8000× downscaling using a flexible equation for high-resolution global dasymetric population modeling. The assessment is applied in New York City, where large spatial heterogeneities exist across confined geographic areas. Results confirm that the performance of the model generally improves as: (i) the binary masking variable becomes increasingly limiting; and, (ii) the density variable becomes more pronounced. However, application requires careful consideration due to their propensity to amplify both positive results and errors.     

TanGeoMS: tangible geospatial modeling system

We present TanGeoMS, a tangible geospatial modeling visualization system that couples a laser scanner, projector, and a flexible physical three-dimensional model with a standard geospatial information system (GIS) to create a tangible user interface for terrain data. TanGeoMS projects an image of real-world data onto a physical terrain model. Users can alter the topography of the model by modifying the clay surface or placing additional objects on the surface. The modified model is captured by an overhead laser scanner then imported into a GIS for analysis and simulation of real-world processes. The results are projected back onto the surface of the model providing feedback on the impact of the modifications on terrain parameters and simulated processes. Interaction with a physical model is highly intuitive, allowing users to base initial design decisions on geospatial data, test the impact of these decisions in GIS simulations, and use the feedback to improve their design. We demonstrate the system on three applications: investigating runoff management within a watershed, assessing the impact of storm surge on barrier islands, and exploring landscape rehabilitation in military training areas.     

Acting responsibly with geospatial data

Geospatial data is simply a "language of the landscape;" it can, for the occurrence of every event, "provide position-based knowledge". It consists of "information that identifies the geographic location, and characteristics of natural or constructed features and boundaries on the earth. This information may be derived from, among other things, remote sensing, mapping, and surveying technologies: Statistical data may be included in this definition at the discretion of the collecting agency." To the extent that such data is time-sensitive and focused on operational deployments, movements, and schedules, it can originate from widely available portable technologies. Statistics and other data are increasingly important because they permit the creation of profiles with myriad uses. Organizations must recognize that geospatial data can be created to contain highly sensitive data and that responsible handling of such data will not detract from a firm's commercial opportunities-in fact, it could help it avert severe reputation damage. Originating organizations will find that as the data they handle become increasingly sensitive, the procedures for deciding whether to withhold or change such data before their release must be well-established and periodically revised to ensure that organizations handle such data responsibly.     

A hybrid classification scheme for mining multisource geospatial data

Supervised learning methods such as Maximum Likelihood (ML) are often used in land cover (thematic) classification of remote sensing imagery. ML classifier relies exclusively on spectral characteristics of thematic classes whose statistical distributions (class conditional probability densities) are often overlapping. The spectral response distributions of thematic classes are dependent on many factors including elevation, soil types, and ecological zones. A second problem with statistical classifiers is the requirement of the large number of accurate training samples (10 to 30 × |dimensions|), which are often costly and time consuming to acquire over large geographic regions. With the increasing availability of geospatial databases, it is possible to exploit the knowledge derived from these ancillary datasets to improve classification accuracies even when the class distributions are highly overlapping. Likewise newer semi-supervised techniques can be adopted to improve the parameter estimates of the statistical model by utilizing a large number of easily available unlabeled training samples. Unfortunately, there is no convenient multivariate statistical model that can be employed for multisource geospatial databases. In this paper we present a hybrid semi-supervised learning algorithm that effectively exploits freely available unlabeled training samples from multispectral remote sensing images and also incorporates ancillary geospatial databases. We have conducted several experiments on Landsat satellite image datasets, and our new hybrid approach shows over 24% to 36% improvement in overall classification accuracy over conventional classification schemes.     

Regional scale cropland carbon budgets: Evaluating a geospatial agricultural modeling system using inventory data

Accurate quantification and clear understanding of regional scale cropland carbon (C) cycling is critical for designing effective policies and management practices that can contribute toward stabilizing atmospheric CO₂ concentrations. However, extrapolating site-scale observations to regional scales represents a major challenge confronting the agricultural modeling community. This study introduces a novel geospatial agricultural modeling system (GAMS) exploring the integration of the mechanistic Environmental Policy Integrated Climate model, spatially-resolved data, surveyed management data, and supercomputing functions for cropland C budgets estimates. This modeling system creates spatially-explicit modeling units at a spatial resolution consistent with remotely-sensed crop identification and assigns cropping systems to each of them by geo-referencing surveyed crop management information at the county or state level. A parallel computing algorithm was also developed to facilitate the computationally intensive model runs and output post-processing and visualization. We evaluated GAMS against National Agricultural Statistics Service (NASS) reported crop yields and inventory estimated county-scale cropland C budgets averaged over 2000–2008. We observed good overall agreement, with spatial correlation of 0.89, 0.90, 0.41, and 0.87, for crop yields, Net Primary Production (NPP), Soil Organic C (SOC) change, and Net Ecosystem Exchange (NEE), respectively. However, we also detected notable differences in the magnitude of NPP and NEE, as well as in the spatial pattern of SOC change. By performing crop-specific annual comparisons, we discuss possible explanations for the discrepancies between GAMS and the inventory method, such as data requirements, representation of agroecosystem processes, completeness and accuracy of crop management data, and accuracy of crop area representation. Based on these analyses, we further discuss strategies to improve GAMS by updating input data and by designing more efficient parallel computing capability to quantitatively assess errors associated with the simulation of C budget components. The modularized design of the GAMS makes it flexible to be updated and adapted for different agricultural models so long as they require similar input data, and to be linked with socio-economic models to understand the effectiveness and implications of diverse C management practices and policies.     

An authorization model for geospatial data

The advent of commercial observation satellites in the new millennium provides unprecedented access to timely information, as they produce images of the Earth with the sharpness and quality previously available only from US, Russian, and French military satellites. Due to the fact that they are commercial in nature, a broad range of government agencies (including international), the news media, businesses, and nongovernmental organizations can gain access to this information. This may have grave implications on national security and personal privacy. Formal policies for prohibiting the release of imagery beyond a certain resolution, and notifying when an image crosses an international boundary or when such a request is made, are beginning to emerge. Access permissions in this environment are determined by both the spatial and temporal attributes of the data, such as location, resolution level, and the time of image download, as well as those of the user credentials. Since existing authorization models are not adequate to provide access control based on spatial and temporal attributes, in this paper, we propose a geospatial data authorization model (GSAM). Unlike the traditional access control models where authorizations are specified using subjects and objects, authorizations in GSAM are specified using credential expressions and object expressions. GSAM supports privilege modes including view, zoom-in, download, overlay, identify, animate, and fly by, among others. We present our access control prototype system that enables subject, object as well as authorization specification via a Web-based interface. When an access request is made, the access control system computes the overlapping region of the authorization and the access request. The zoom-in and zoom-out requests can simply be made through a click of the mouse, and the appropriate authorizations will be evaluated when these access requests are made     

A geosemantic proximity-based prototype for the interoperability of geospatial data

The research agenda related to the interoperability of geospatial data is influenced by the increased accessibility of geospatial databases on the Internet, as well as their sharing and their integration. Although it is now possible to get and use geospatial data independently of their syntax and structure, it is still difficult for users to find the exact data they need as long as they do not know the precise vocabulary used by the organizations supporting geospatial databases. It is now a necessity to take into consideration the semantics of geospatial data to enable its full interoperability.To this end, we designed a new conceptual framework for geospatial data interoperability and introduced the notion of geosemantic proximity based on human communication and cognition paradigms. This paper reviews this framework and the notion of geosemantic proximity. It also presents the GsP Prototype, which demonstrates the relevance of our framework and of the notion of geosemantic proximity for geospatial data interoperability. More specifically, we describe the architecture of the GsP Prototype, its implementation, and tests that have been conducted.     

基于科学工作流的空间数据处理技术探讨

首先介绍了与地理空间数据处理相关的科学工作流技术,并与传统商业工作流技术作对比,然后以Kepler项目为例说明科学工作流技术在空间数据处理领域的应用,最后讨论目前存在的问题并给出了结论。     

空间数据网络处理服务模型及关键技术

面向空间数据处理的服务描述、部署、发现、调用过程是空间数据服务化处理的关键问题,直接关系到空间分析与相关数据处理计算的实现方式和执行效率。在标准网络服务模式之上,参照OGC规范设计空间数据网络过程处理服务的实现模型。并在空间数据分析和网络处理服务模型基础上,对网络服务的资源结构、服务调用模式、空间分析函数、数据处理流程等部分给出设计和定义。并以空间缓冲区分析算法为实例,实现过程处理服务模型实例,并给出分布式网络环境下空间数据处理服务的发布、调用与计算模式的完整实现方法。     

基于文档数据库的全栈式地理空间数据传播模型*

针对基于关系型数据库的地理信息传播模型在互联网虚拟计算环境下的不足,提出了一种基于文档数据库的全栈式地理信息传播模型。该模型以多粒度地理特征的映射规约分析计算和文档数据库的多版本并发控制为框架,用统一的内建域指定语言作为传播媒介描述载体,结合数据的运行时元编程和地理服务的REST风格部署,实现地理空间数据的动态开放式传播,证明非结构化地理空间数据的传播模型能满足逻辑语义和物理存储双重易扩展性。     

数据库系统性能模型建模方法综述

数据库系统性能模型是数据库系统管理的重要基础技术支撑,广泛用于查询调度、资源分配、性能调优等任务中。当前的性能模型主要分为分析型和统计型两种,分析型模型需要深入研究数据库系统查询执行过程,对动态查询的适应性较好,无须成本高昂的采样实验,但在查询并行执行情景下建模复杂,对不同的数据库系统有不同的理论模型。统计型模型无须分析查询执行过程,通过采集查询执行参数并训练某个数学模型。统计型建模过程简单,能够较好地描述查询交互,预测效果较好,但采样成本很高,对动态查询的适应性差。对数据库系统性能建模的主要文献进行综述,重点介绍数据库系统性能建模的主要方法,并讨论这两类模型各自的优缺点、建模的难点以及应对策略。在此基础上,对数据库系统性能模型领域的研究做了展望,为有关该领域的研究提供参考。     

Modeling geospatial trend changes in vegetation monitoring data

In a constantly changing environment, monitoring supports analysis and understanding of many types of change. This paper is concerned specifically with monitoring of vegetation and describes the development and application of a formal model that supports the analysis of spatiotemporal changes in the recorded attributes of a forest/heathland environment. Typically, the monitoring points are not ideally distributed in time or space. The proposed analytical techniques are designed to deal with incomplete data sets and to reveal abnormal changes or transitions. These transitions can potentially be linked to causal events which may have not been otherwise recorded. This work distinguishes five key change types as a basis for 25 transition types present in time series of vegetation data. These are distilled from the data using a set of transition point analysis methods including spatiotemporal neighborhood and trend sequence analysis. In addition, cross-comparisons between vegetation attributes, based on the identified transitions, are illustrated. A prototype GIS-based tool VeMonA provides an analytical environment for time series data obtained through vegetation monitoring and supports understanding of dynamic geospatial ecosystems.     

A data mining approach to database compression

Data mining can dig out valuable information from databases to assist a business in approaching knowledge discovery and improving business intelligence. Database stores large structured data. The amount of data increases due to the advanced database technology and extensive use of information systems. Despite the price drop of storage devices, it is still important to develop efficient techniques for database compression. This paper develops a database compression method by eliminating redundant data, which often exist in transaction database. The proposed approach uses a data mining structure to extract association rules from a database. Redundant data will then be replaced by means of compression rules. A heuristic method is designed to resolve the conflicts of the compression rules. To prove its efficiency and effectiveness, the proposed approach is compared with two other database compression methods. Chin-Feng Lee is an associate professor with the Department of Information Management at Chaoyang University of Technology, Taiwan, R.O.C. She received her M.S. and Ph.D. degrees in 1994 and 1998, respectively, from the Department of Computer Science and Information Engineering at National Chung Cheng University. Her current research interests include database design, image processing and data mining techniques. S. Wesley Changchien is a professor with the Institute of Electronic Commerce at National Chung-Hsing University, Taiwan, R.O.C. He received a BS degree in Mechanical Engineering (1989) and completed his MS (1993) and Ph.D. (1996) degrees in Industrial Engineering at State University of New York at Buffalo, USA. His current research interests include electronic commerce, internet/database marketing, knowledge management, data mining, and decision support systems. Jau-Ji Shen received his Ph.D. degree in Information Engineering and Computer Science from National Taiwan University at Taipei, Taiwan in 1988. From 1988 to 1994, he was the leader of the software group in Institute of Aeronautic, Chung-Sung Institute of Science and Technology. He is currently an associate professor of information management department in the National Chung Hsing University at Taichung. His research areas focus on the digital multimedia, database and information security. His current research areas focus on data engineering, database techniques and information security. Wei-Tse Wang received the B.A. (2001) and M.B.A (2003) degrees in Information Management at Chaoyang University of Technology, Taiwan, R.O.C. His research interests include data mining, XML, and database compression.     

Exploratory geospatial data analysis using the GeoSOM suite

Clustering constitutes one of the most popular and important tasks in data analysis. This is true for any type of data, and geographic data is no exception. In fact, in geographic knowledge discovery the aim is, more often than not, to explore and let spatial patterns surface rather than develop predictive models. The size and dimensionality of the existing and future databases stress the need for efficient and robust clustering algorithms. This need has been successfully addressed in the context of general-purpose knowledge discovery. Geographic knowledge discovery, nonetheless can still benefit from better tools, especially if these tools are able to integrate geographic information and aspatial variables in order to assist the geographic analyst’s objectives and needs. Typically, the objectives are related with finding spatial patterns based on the interaction between location and aspatial variables. When performing cluster-based analysis of geographic data, user interaction is essential to understand and explore the emerging patterns, and the lack of appropriate tools for this task hinders a lot of otherwise very good work.     

Visual data mining in large geospatial point sets

Visual data-mining techniques have proven valuable in exploratory data analysis, and they have strong potential in the exploration of large databases. Detecting interesting local patterns in large data sets is a key research challenge. Particularly challenging today is finding and deploying efficient and scalable visualization strategies for exploring large geospatial data sets. One way is to share ideas from the statistics and machine-learning disciplines with ideas and methods from the information and geo-visualization disciplines. PixelMaps in the Waldo system demonstrates how data mining can be successfully integrated with interactive visualization. The increasing scale and complexity of data analysis problems require tighter integration of interactive geospatial data visualization with statistical data-mining algorithms.     

Large scale geospatial data conflation: A feature matching framework based on optimization and divide-and-conquer

Geospatial data conflation is the process of combining two datasets to create a better one. It has received increased research attention due to the emergence of new data sources and the need to combine information from these sources in spatial analyses. Many conflation methods exist to date, ranging from simple ones based on spatial join, to sophisticated methods based on statistics and optimization models. This paper focuses on the optimization-based conflation approach. It treats feature-matching in conflation as an optimization problem of finding a plan to match features in two datasets that minimizes the total discrepancy. Optimization based conflation methods may overcome some limitations of conventional methods, such as sub-optimality and greediness. However, they have often been deemed impractical in day-to-day analysis because they induce high computational costs (especially in combining large geospatial data).In this paper, we demonstrate the feasibility of performing optimization-based conflation for large geographic data in Geographic Information Systems. This is accomplished by utilizing efficient network flow-based conflation models and a divide-and-conquer strategy that allows the conflation models to scale to large data. Experiments show that the network-flow based model achieves average recall and precision rates of 97.7% and 90.8%, respectively in small test areas, and outperforms the traditional assignment problem by about 9% each. For larger data, it took the original network-flow model (without divide-and-conquer) nearly two days to conflate the road network in a portion of Los Angeles area near the LAX international airport. By contrast, the same model can be used to conflate the road networks of the entire Los Angeles County, CA in under 3 h with the divide and conquer strategy.     

Real-time landscape model interaction using a tangible geospatial modeling environment

Emerging technologies that combine the flexibility of digital landscape representation with easy-to-interpret 3D physical models open new possibilities for user interaction with geospatial data. A prototype tangible geospatial modeling environment lets users interact with landscape analysis and simulations using a tangible physical model. We introduce a concept that builds upon previous independent tangible user interface (TUI) and terrain analysis research and aims at more intuitive collaborative interaction with digital landscape data.