Huayi Wu

Using spatial principles to optimize distributed computing for enabling the physical science discoveries

Contemporary physical science studies rely on the effective analyses of geographically dispersed spatial data and simulations of physical phenomena. Single computers and generic high-end computing are not sufficient to process the data for complex physical science analysis and simulations, which can be successfully supported only through distributed computing, best optimized through the application of spatial principles. Spatial computing, the computing aspect of a spatial cyberinfrastructure, refers to a computing paradigm that utilizes spatial principles to optimize distributed computers to catalyze advancements in the physical sciences. Spatial principles govern the interactions between scientific parameters across space and time by providing the spatial connections and constraints to drive the progression of the phenomena. Therefore, spatial computing studies could better position us to leverage spatial principles in simulating physical phenomena and, by extension, advance the physical sciences. Using geospatial science as an example, this paper illustrates through three research examples how spatial computing could (i) enable data intensive science with efficient data/services search, access, and utilization, (ii) facilitate physical science studies with enabling high-performance computing capabilities, and (iii) empower scientists with multidimensional visualization tools to understand observations and simulations. The research examples demonstrate that spatial computing is of critical importance to design computing methods to catalyze physical science studies with better data access, phenomena simulation, and analytical visualization. We envision that spatial computing will become a core technology that drives fundamental physical science advancements in the 21st century.

A virtual globe-based 3D visualization and interactive framework for public participation in urban planning processes

Abstract Public participation is very important for the success of an urban planning project, since any urban planning project will ultimately become part of the everyday life of the public. Most members of the general public are not urban planning professionals; therefore, well-designed visualization and interactive tools can help expand their participation in urban planning processes. The emerging technology of virtual globe-based 3D visualization is a unique opportunity to facilitate public participation in urban planning projects by promoting intuitive 3D interaction, instant interoperability and seamless integration of 3D visualization with other traditional text and multimedia information channels. This paper discusses the technical issues of developing a virtual globe-based 3D visualization framework for publicizing urban planning information, using Web Services and Service Oriented Architecture (SOA) to support visual planning model sharing and interoperability. With 3D photorealistic visualization, end users can conveniently obtain both the macro-vision of a project on the global scale and the micro-details on the street scale, using swift zooming tools like Google Earth. End users can select any available urban planning solution for visual investigation and comparison in a virtual globe-based 3D visualization environment. The service oriented architecture allows urban planning information to be deployed as a service in one server or several geographically distributed servers, or even from the end user’s own computer. With the architecture’s capability for integrating distributed resources, other traditional interactive functions such as labeling, BBS, forum, and email, can also be conveniently integrated into the system. Auxiliary spatial analysis tools are integrated to help end users perform “professional” tasks such as sunlight analysis and 3D distance measurement. This highly distributed system is designed for the Internet; therefore, any personal computer connected to the Internet can easily access the system and participate in the interaction.

An optimized framework for seamlessly integrating OGC Web Services to support geospatial sciences

OGC Web Services (OWS) are essential building blocks for the national and global spatial data infrastructure (NSDI and GSDI) and the geospatial cyberinfrastructure (GCI). Web Map Service (WMS), Web Feature Service (WFS), Web Coverage Service (WCS), and Catalogue Service for Web (CSW) have been increasingly adopted to serve scientific data. Interoperable services can facilitate the integration of different scientific applications by searching, finding, and utilizing the large number of scientific data and Web services. However, these services are widely dispersed and hard to be found and utilized with acceptable performance. This is especially true when developing a science application to seamlessly integrate multiple geographically dispersed services. Focusing on the integration of distributed OWS resources, we propose a layer-based service-oriented integration framework and relevant optimization technologies to search and utilize relevant resources. Specifically, (1) an AJAX (Asynchronous JAvaScript and eXtensible Markup Language)-based synchronous multi-catalogue search is proposed and utilized to enhance the multi-catalogue searching performance; (2) a layer-based search engine with spatial, temporal, and performance criteria is proposed and used for identifying better services; (3) a service capabilities clearinghouse (SCCH) is proposed and developed to address the service issues identified by a statistical experiment. A science application of data correlation analysis is used as an example to demonstrate the performance enhancement of the proposed framework.

Leveraging the power of multi-core platforms for large-scale geospatial data processing: Exemplified by generating DEM from massive LiDAR point clouds

In recent years improvements in spatial data acquisition technologies, such as LiDAR, resulted in an explosive increase in the volume of spatial data, presenting unprecedented challenges for computation capacity. At the same time, the kernel of computing platforms the CPU, also evolved from a single-core to multi-core architecture. This radical change significantly affected existing data processing algorithms. Exemplified by the problem of generating DEM from massive air-borne LiDAR point clouds, this paper studies how to leverage the power of multi-core platforms for large-scale geospatial data processing and demonstrates how multi-core technologies can improve performance. Pipelining is adopted to exploit the thread level parallelism of multi-core platforms. First, raw point clouds are partitioned into overlapped blocks. Second, these discrete blocks are interpolated concurrently on parallel pipelines. On the interpolation run, intermediate results are sorted and finally merged into an integrated DEM. This parallelization demonstrates the great potential of multi-core platforms with high data throughput and low memory footprint. This approach achieves excellent performance speedup with greatly reduced processing time. For example, on a 2.0GHz Quad-Core Intel Xeon platform, the proposed parallel approach can process approximately one billion LiDAR points (16.4GB) in about 12min and produces a 27,500x30,500 raster DEM, using less than 800MB main memory.

Monitoring and evaluating the quality of Web Map Service resources for optimizing map composition over the internet to support decision making

Over the past 10 years, there have been great advances in the interoperability technologies in geographic information science. More than 10,000 map layers are available online today through Open Geospatial Consortium (OGC) specified interfaces, such as Web Map Service (WMS), Web Feature Service (WFS), and Web Coverage Service (WCS). These map layers are persistently serving the geospatial communities; however, our empirical study found that their potential value has not been fully exploited. Frequently, a targeted map cannot be composed because some published map servers are unavailable. This problem becomes more serious when a map is composed of several layers from different servers. These services are geographically distributed and maintained by various hosts; therefore, simply waiting for service improvement on the host side cannot solve this problem. In this paper, we proposed a new approach and developed a mechanism that allows clients to select the best map layers at run-time. The selection is based on the results of continuous monitoring and evaluation of the quality of WMSs. Based on Service Oriented Architecture (SOA), this approach includes quality monitoring and evaluation modules. Quality factors are taken into account during the process of registration, search, and bind. The OGC capability document is extended to include WMS quality information. Three prototype systems were developed in this study to demonstrate: (a) how WMS layers are monitored and evaluated, (b) how the subjective evaluation of WMS quality by a user is collected, and (c) how this can be a feasible method to fuse WMS resources suitable for decision making.

Semantic-based web service discovery and chaining for building an Arctic spatial data infrastructure

Increasing interests in a global environment and climate change have led to studies focused on the changes in the multinational Arctic region. To facilitate Arctic research, a spatial data infrastructure (SDI), where Arctic data, information, and services are shared and integrated in a seamless manner, particularly in light of todays climate change scenarios, is urgently needed. In this paper, we utilize the knowledge-based approach and the spatial web portal technology to prototype an Arctic SDI (ASDI) by proposing (1) a hybrid approach for efficient service discovery from distributed web catalogs and the dynamic Internet; (2) a domain knowledge base to model the latent semantic relationships among scientific data and services; and (3) an intelligent logic reasoning mechanism for (semi-)automatic service selection and chaining. A study of the influence of solid water dynamics to the bio-habitat of the Arctic region is used as an example to demonstrate the prototype.

Cloud computing for geosciences: deployment of GEOSS clearinghouse on Amazon's EC2

To test the utilization of cloud computing for Geosciences applications, the GEOSS clearinghouse was deployed, maintained and tested on the Amazon Elastic Cloud Computing (EC2) platform. The GEOSS Clearinghouse is a web based Geographic Metadata Catalog System, which manages millions of the metadata of the spatially referenced resources for the Global Earth Observations (GEO). Our experiment reveals that the EC2 cloud computing platform facilitates geospatial applications in the aspects of a) scalability, b) reliability, and c) reducing duplicated efforts among Geosciences communities. Our test of massive data inquiry by concurrent user requests proves that different applications should be justified and optimized when deploying onto the EC2 platform for a better balance of cost and performance.

Spatial data quality and beyond

Issues of accuracy, uncertainty, and spatial data quality have been on the top of most GIScience research agendas around the world from the late 1980s. Ever since then, growing research efforts have been directed toward uncertainty characterization in spatial information, analysis, and applications, aiming for better understanding of spatial uncertainty and thus improved methods and techniques for assessing and managing data quality. Impressive progress has been made in various issues concerning data quality. In addition, growing research on extensions to the conventional norms of data quality, such as the quality aspects of geospatial information services, has been observed. Chinese researchers have contributed to this great cause by keeping abreast with the developments abroad and striving for their own innovative work. This paper reviews the past research on data quality-related issues and provides a perspective on future developments. These will be seen not only in continued research on theoretical and technical issues concerning data quality, but also in developments of tools for quality assessment and decision-making under uncertainty through geospatial information processing and applications.

Visualizing dynamic geosciences phenomena using an octree-based view-dependent LOD strategy within virtual globes

Abstract Geoscientists build dynamic models to simulate various natural phenomena for a better understanding of our planet. Interactive visualizations of these geoscience models and their outputs through virtual globes on the Internet can help the public understand the dynamic phenomena related to the Earth more intuitively. However, challenges arise when the volume of four-dimensional data (4D), 3D in space plus time, is huge for rendering. Datasets loaded from geographically distributed data servers require synchronization between ingesting and rendering data. Also the visualization capability of display clients varies significantly in such an online visualization environment; some may not have high-end graphic cards. To enhance the efficiency of visualizing dynamic volumetric data in virtual globes, this paper proposes a systematic framework, in which an octree-based multiresolution data structure is implemented to organize time series 3D geospatial data to be used in virtual globe environments. This framework includes a view-dependent continuous level of detail (LOD) strategy formulated as a synchronized part of the virtual globe rendering process. Through the octree-based data retrieval process, the LOD strategy enables the rendering of the 4D simulation at a consistent and acceptable frame rate. To demonstrate the capabilities of this framework, data of a simulated dust storm event are rendered in World Wind, an open source virtual globe. The rendering performances with and without the octree-based LOD strategy are compared. The experimental results show that using the proposed data structure and processing strategy significantly enhances the visualization performance when rendering dynamic geospatial phenomena in virtual globes.

An Improved Top-Hat Filter with Sloped Brim for Extracting Ground Points from Airborne Lidar Point Clouds

Airborne light detection and ranging (lidar) has become a powerful support for acquiring geospatial data in numerous geospatial applications and analyses. However, the process of extracting ground points accurately and effectively from raw point clouds remains a big challenge. This study presents an improved top-hat filter with a sloped brim to enhance the robustness of ground point extraction for complex objects and terrains. The top-hat transformation is executed and the elevation change intensity of the transitions between the obtained top-hats and outer brims is inspected to suppress the omission error caused by protruding terrain features. Finally, the nonground objects of complex structures, such as multilayer buildings, are identified by the brim filter that is extended outward. The performance of the proposed filter in various environments is evaluated using diverse datasets with difficult cases. The comparison of the proposed filter with the commercial software Terrasolid TerraScan and other popular filtering algorithms demonstrates the applicability and effectiveness of this filter. Experimental results show that the proposed filter has great promise in terms of its application in various types of landscapes. Abrupt terrain features with dramatic elevation changes are well preserved, and diverse objects with complicated shapes are effectively removed. This filter has minimal omission and commission error oscillation for different test areas and thus demonstrates a stable and reliable performance in diverse landscapes. In addition, the proposed algorithm has high computational efficiency because of its simple and efficient data structure and implementation.