Concurrent Drainage Network Rendering for Automated Pen‐and‐ink Style Landscape Illustration

Pen‐and‐ink style geomorphological illustrations render landscape elements critical to the understanding of surface processes within a viewshed and, at their highest levels of execution, represent works of art, being both practical and beautiful. The execution of a pen‐and‐ink composition, however, requires inordinate amounts of time and skill. This article will introduce an algorithm for rendering creases – linework representing visually significant morphological features – at animation speeds, made possible with recent advances in graphics processing unit (GPU) architectures and rendering APIs. Beginning with a preprocessed high‐resolution drainage network model, creases are rendered from selected stream segments if their weighted criteria (slope, flow accumulation, and surface illumination), attenuated by perspective distance from the viewpoint, exceed a threshold. The algorithm thus provides a methodology for crease representation at continuous levels of detail down to the highest resolution of the preprocessed drainage model over a range of surface orientation and illumination conditions. The article also presents an implementation of the crease algorithm with frame rates exceeding those necessary to support animation, supporting the proposition that parallel processing techniques exposed through modern GPU programming environments provide cartographers with a new and inexpensive toolkit for constructing alternative and attractive real‐time animated landscape visualizations for spatial analysis.     

Automating Landscape Illustration with Pen and Ink Style Rendering

This paper introduces object space procedures for extracting silhouettes, slope lines, and drainage features from digital elevation models (DEMs) to direct the rendering of landform features in the style of pen-and-ink landscape illustrations. Unlike image space procedures which generally extract feature information through 2-dimensional (2D) rendered image processing techniques, object space procedures operate directly on 3-dimensional (3D) surface models. The ultimate goal of this work is to produce fully automated tools that can imitate the styles characterized by the traditional pen-and-ink techniques of Lobeck, Imhof, Raisz, and other scientific illustrators to bring the beauty of their effective and economical visual techniques to automated cartographic environments. Through the implementation of a Java 3D application programming interface (API) prototype, a testing platform was established for the application of stylistic elements to linework representing surface form lines. This paper explores the aesthetic effects of silhouette lines, creases, and slope lines on the rendering of terrain features. It also introduces the use of adaptively resampled triangulated irregular networks (TINs) as a basis for perspective rendering applications.     

Supporting Automated Pen and Ink Style Surface Illustration with B-Spline Models

This paper introduces a methodology for constructing pen and ink style landscape illustrations using B-spline surface models. Following a brief discussion of manual methods and recent developments in automated non-photorealistic rendering (NPR) techniques, the paper discusses polynomial models for surface rendering and their implementation in the OpenGL graphics interface. It then discusses the generation of silhouettes and form lines for topographic surfaces from a set of triangulated vertices obtained from polygonal tessellations. An implementation of the methodology outlined in the paper is described and tested. The results are analyzed and suggestions for further research are presented.     

A Multi‐granularity Parallel Model for Unified Remote Sensing Image Processing WebServices

The growth of the Web has resulted in the Web‐based sharing of distributed geospatial data and computational resources. The Geospatial Processing Web (GeoPW) described here is a set of services that provide a wide array of geo‐processing utilities over the Web and make geo‐processing functionalities easily accessible to users. High‐performance remote sensing image processing is an important component of the GeoPW. The design and implementation of high‐performance image processing are, at present, an actively pursued research topic. Researchers have proposed various parallel strategies for single image processing algorithm, based on a computer science approach to parallel processing. This article proposes a multi‐granularity parallel model for various remote sensing image processing algorithms. This model has four hierarchical interfaces that are labeled the Region of Interest oriented (ROI‐oriented), Decompose/Merge, Hierarchical Task Chain and Dynamic Task interfaces or sub‐models. In addition, interfaces, definitions, parallel task scheduling and fault‐tolerance mechanisms are described in detail. Based on the model and methods, we propose an open‐source online platform named OpenRS‐Cloud. A number of parallel algorithms were uniformly and efficiently developed, thus certifying the validity of the multi‐granularity parallel model for unified remote sensing image processing web services.     

Tessellations in GIS: Part II–making changes

Abstract

We attempt to describe the role of tessellated models of space within the discipline of Geographic Information Systems (GIS) – a speciality coming largely out of Geography and Land Surveying, where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps. We look at some of the basic operations in GIS, including dynamic and kinetic applications. We examine issues of topology and data structures, and produced a tessellation model that may be widely applied both to traditional “object” and “field” data types. The Part I of this study examined object and field spatial models, the Voronoi extension of objects, and the graphs that express the resulting adjacencies. The required data structures were also briefly described, along with 2D and 3D structures and hierarchical indexing. The importance of graph duality was emphasized. Here, this second paper builds on the structures described in the first, and examines how these may be modified: change may often be associated with either viewpoint or time. Incremental algorithms permit additional point insertion, and applications involving the addition of skeleton points, for map scanning, contour enrichment or watershed delineation and simulation. Dynamic algorithms permit skeleton smoothing, and higher order Voronoi diagram applications, including Sibson interpolation. Kinetic algorithms allow collision detection applications, free-Lagrange flow modeling, and pen movement simulation for map drawing. If desired these methods may be extended to 3D. Based on this framework, it can be argued that tessellation models are fundamental to our understanding and processing of geographical space, and provide a coherent framework for understanding the “space” in which we exist.     

An effective spherical panoramic LoD model for a mobile street view service

Street view (panoramic view) services have begun to shift from PC platforms to mobile smart devices. Considering the capabilities of smart terminal devices, a 360° spherical level of detail (LoD) model for mobile street view services that renders panoramic images on the inner surface of a sphere via LoD is proposed in this article. Panoramic images are segmented into tiles and organized in a pyramid tile structure for LoD rendering to improve the rendering efficiency of the proposed model. A projection model between panoramic images and the spherical surface is presented to map the panoramic tiles on the spherical graticule. A street view‐rendering algorithm of panoramic images is proposed with the rendering function of OpenGL for Embedded Systems (OpenGL ES). A street view service app running on Android, based on the proposed approach, is implemented to assess two aspects of the panoramic view model, namely visualization effect and efficiency. Experiment results reveal that the 360° spherical LoD model of the panoramic image can display 3D street view scenes better than the cubic panoramic view model. The proposed model also has an excellent 3D visualization effect and high‐efficiency rendering ability for mobile street view services. Therefore, it is applicable in large‐scale mobile street view services both online and offline and in augmented reality navigation.     

Possibilities of using raster data in client‐side web maps

Companies and individual developers have recently put serious effort into improving web mapping libraries. A significant front in this development is hardware‐accelerated vector graphics. Owing to those efforts, and the continuously evolving World Wide Web, users can visualize large vector layers, and even animate them. On the other hand, this rapid development cannot be observed with raster data. There are some data abstraction libraries for reading raster files, although web mapping libraries do not use them to offer raster capabilities. Since there are no mature raster management pipelines on the web, this study explores two inherently different techniques for handling raster data. One of them uses the traditional, texture‐based method. The other is a hybrid technique rendering raster layers as vectors, overcoming some limitations of the raster model. While the traditional technique gives a smooth user experience, the hybrid method shows promising results for rendering hexagonal coverages.     

Tile‐based polygon overlay of vector data in a virtual globe

The demand for analysis of large‐scale data has increased with increased access to spatial vector data. Polygon overlay of vector data in a virtual globe requires proprietary data structures and proprietary analysis algorithms. A vector data structure is designed for rapid polygon overlay in a virtual globe by recording the metadata of the triangles (TriMeta) that constitute polygons. A polygon overlay algorithm is proposed based on this data structure. The overlay of two complex GIS polygons is transformed into the intersection computation of their 3D triangular presentations. The intersection computation of two sets of triangles is reduced considerably by utilizing TriMeta to filter out the disjoint triangle pairs and rapidly identify the contained triangle. The new method improves the overlay efficiency in a virtual globe because the amount of computation required to calculate the intersections of two large polygons and drape the intersections onto a terrain surface is reduced.     

Out‐of‐Core GPU‐based Change Detection in Massive 3D Point Clouds

If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out‐of‐core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point‐based rendering technique adapted for attributed 3D point clouds, to enable effective out‐of‐core real‐time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.     

基于GPGPU的并行影像匹配算法

提出一种基于GPGPU的CUDA架构快速影像匹配并行算法,它能够在SIMT模式下完成高性能并行计算。并行算法根据GPU的并行结构和硬件特点,采用执行配置技术、高速存储技术和全局存储技术三种加速技术,优化数据存储结构,提高数据访问效率。实验结果表明,并行算法充分利用GPU的并行处理能力,在处理1280×1024分辨率的8位灰度图像时可达到最高多处理器warp占有率,速度是基于CPU实现的7倍。CUDA在高运算强度数据处理中呈现出的实时处理能力和计算能力,为进一步加速影像匹配性能和GPU通用计算提供了新的方法和思路。     

A Patch‐based Cellular Automaton for Simulating Land‐use Changes at Fine Spatial Resolution

While cellular automata have become popular tools for modeling land‐use changes, there is a lack of studies reporting their application at very fine spatial resolutions (e.g. 5 m resolution). Traditional cell‐based CA do not generate reliable results at such resolutions because single cells might only represent components of land‐use entities (i.e. houses or parks in urban residential areas), while recently proposed entity‐based CA models usually ignore the internal heterogeneity of the entities. This article describes a patch‐based CA model designed to deal with this problem by integrating cell and object concepts. A patch is defined as a collection of adjacent cells that might have different attributes, but that represent a single land‐use entity. In this model, a transition probability map was calculated at each cell location for each land‐use transition using a weight of evidence method; then, land‐use changes were simulated by employing a patch‐based procedure based on the probability maps. This CA model, along with a traditional cell‐based model were tested in the eastern part of the Elbow River watershed in southern Alberta, Canada, an area that is under considerable pressure for land development due to its proximity to the fast growing city of Calgary. The simulation results for the two models were compared to historical data using visual comparison, K_simulation indices, and landscape metrics. The results reveal that the patch‐based CA model generates more compact and realistic land‐use patterns than the traditional cell‐based CA. The K_simulation values indicate that the land‐use maps obtained with the patch‐based CA are in higher agreement with the historical data than those created by the cell‐based model, particularly regarding the location of change. The landscape metrics reveal that the patch‐based model is able to adequately capture the land‐use dynamics as observed in the historical data, while the cell‐based CA is not able to provide a similar interpretation. The patch‐based approach proposed in this study appears to be a simple and valuable solution to take into account the internal heterogeneity of land‐use classes at fine spatial resolutions and simulate their transitions over time.     

Parallel algorithm for viewshed analysis on a modern GPU

Spatial analysis, including viewshed analysis, is an important aspect of the Digital Earth system. Viewshed analysis is usually performed on a large scale, so efficiency is important in any Digital Earth application making these calculations. In this paper, a real-time algorithm for viewshed analysis in 3D scenes is presented by using the parallel computing capabilities of a graphics processing unit (GPU). In contrast to traditional algorithms based on line-of-sight, this algorithm runs completely within the programmable 3D visualization pipeline to render 3D terrains with viewshed analysis. The most important difference is its integration of the viewshed calculation with the rendering module. Invisible areas are rendered as shadows in the 3D scene. The algorithm process is paralleled by rasterizer units in the graphics card and by vertex and pixel shaders executed on the GPU. We have implemented this method in our 3D Digital Earth system with the DirectX 9.0c API and tested on some consumer-level PC platforms with interactive frame-rates and high image quality. Our algorithm has been widely used in related systems based on Digital Earth.     

基于对等网络的多分辨率影像的网络传输模型

海量影像数据的网络传输和多分辨率显示是实现网络环境下影像图形浏览的关键。利用对等网络结构,借鉴基于P2P的文件下栽相关技术,提出一种基于P2P的影像传输模型。在遥感影像分级分块的基础上,该模型通过构建基于兴趣区域分组的多追踪器覆盖网络,实现了节点加入和邻近节点选择的算法优化。通过模拟试验证明了模型中邻近节点选择的优化性能,并开发了原型系统IMAGEP2P。     

A Conceptual Framework for Uncertainty Investigation in Map‐based Land Cover Change Modelling

Uncertainty research represents a research stream of high interest within the community of geographical information science. Its elements, terminology and typology are still under strong discussion and adopted methods for analysis are currently under intensive development. This paper presents a conceptual framework for systematic investigation of uncertainty which occurs in applications of land cover change modelling in Geographical Information Systems (GIS) based on historical map data. Historical, in this context, means the map is old enough to allow identification of changes in landscape elements of interest, such as vegetation. To date such analyses are rarely conducted or not satisfactorily carried out, despite the fact that historical map data represent a potentially rich information source. The general validity and practicability of the framework for related applications is demonstrated with reference to one example in which forest cover change in Switzerland is investigated. The conceptual model consists of three domains in which main potential sources of uncertainty are systematically exposed. Existing links between data quality research and uncertainty are investigated to access the complex nature of uncertainty and to characterise the most suitable concepts for analysis. In accordance with these concepts appropriate methods and procedures are suggested to assess uncertainty in each domain. One domain is the production‐oriented amount of uncertainty which is inherent in the historical map. Vagueness and ambiguity represent suitable concepts for analysis. Transformation‐oriented uncertainty as the second domain occurs owing to editing and processing of digital data. Thereby, the suitable concept of uncertainty is error. The third domain is the application‐oriented uncertainty which occurs in comparing semantically different data. This domain relates to multi‐temporal discord which assumes the assessment of ‘equi‐temporal’ ambiguity and is thus connected to the production‐oriented domain. The framework provides an estimation of the overall amount of uncertainty. This can be linked to subsequent assessment of ‘fitness for use’. Thus the model provides a practicable and systematic approach to access the complex nature of uncertainty in the scope of land cover change modelling.     

地理信息系统中时空多维数据可视化技术研究

将科学可视化技术应用于地理信息系统之中,增强地理信息系统对时空多维数据的显示,处理胆当前的研究热点之一。通过对现有地理数据组织,显示简便概述的基础上,提出一种基于体素的时空多维数据组织方式。并通过二维,三维显示及动画形式的研究,从技术上对科学可视化与地理信息系统的结合进行初步的探索。     

建筑物三维表面模型简化算法探讨

模型简化是近年来计算机图形学中的一个研究热点,并已在虚拟现实、可视化、实时渲染、计算机视觉和渐进传输等众多方向得到广泛应用。本文针对3D建筑物表面模型的自动简化处理,首先对现有模型简化算法及各自的优缺点进行了归纳总结,分析了现有3D建筑物模型简化算法存在的问题,并探讨了3D建筑物表面模型的简化思路。     

Mapping areas of asynchronous‐temporal interaction in animal‐telemetry data

Animal interactions are a crucial aspect of behavioral ecology that affect mating, territorial behavior, resource use, and disease spread. Commonly, animals will interact because of shared resources. Recent methods have used time geography to map landscape areas where interactions were possible. However, such methods do not identify areas of less direct interaction, like through smell or sight. These indirect or asynchronous interactions are also a crucial aspect of animal behavioral ecology and affect group behaviors such as leading/following hierarchies and joint resource use. Asynchronous interactions are difficult to map because they can occur in a synchronous space at asynchronous times, as well as in asynchronous spaces at a synchronous time. Here, we present a method termed the temporally asynchronous‐joint potential path area (ta‐jPPA) that maps areas of potential temporally asynchronous–spatially synchronous interactions. We used simulated data to statistically test ta‐jPPA and empirical data to demonstrate how ta‐jPPA can find patterns in habitat use.     

《新疆维吾尔自治区资源经济地图集》设计与创新

《新疆维吾尔自治区资源经济地图集》的编制设计是一项涉及面广泛,以地理空间数据集为基础,通过对各种多源数据的分析、处理、加工,以专题地图符号、图表的形式表达新疆地理地貌特征、资源环境保护、自然景观,以及社会经济状况。本文重点探讨了地图集的总体框架设计及专题数据内容选取及数据坐标转换,多源数据融合方法、卫星影像图、鸟瞰图、地貌晕渲、地势、地图专题符号设计、地图集色彩的统一协调等关键技术进行阐述。     

Parallel implementation of endmember extraction algorithms from hyperspectral data

Automated extraction of spectral endmembers is a crucial task in hyperspectral data analysis. In most cases, the computational complexity of endmember extraction algorithms is very high, in particular, for very high-dimensional datasets. However, the intrinsic properties of available techniques are amenable to the design of parallel implementations. In this letter, we evaluate several parallel algorithms that represent three representative approaches to the problem of extracting endmembers. Two parallel algorithms have been selected to represent a first class of algorithms based on convex geometry concepts. In particular, we develop parallel implementations of approximate versions of the N-FINDR and pixel purity index algorithms, along with a parallel hybrid of both techniques. A second class is given by algorithms based on constrained error minimization and represented by a parallel version of the iterative error analysis algorithm. Finally, a parallel version of the automated morphological endmember extraction algorithm is also presented and discussed. This algorithm integrates the spatial and spectral information as opposed to the other discussed algorithms, a feature that introduces additional considerations for its parallelization. The proposed algorithms are quantitatively compared and assessed in terms of both endmember extraction accuracy and parallel efficiency, using standard AVIRIS hyperspectral datasets. Performance data are measured on Thunderhead, a parallel supercomputer at NASA's Goddard Space Flight Center.     

SPAWNN: A Toolkit for SPatial Analysis With Self‐Organizing Neural Networks

This article introduces the SPAWNN toolkit, an innovative toolkit for spatial analysis with self‐organizing neural networks, which is published as free and open‐source software ( http://www.spawnn.org ). It extends existing toolkits in three important ways. First, the SPAWNN toolkit distinguishes between self‐organizing neural networks and spatial context models with which the networks can be combined to incorporate spatial dependence and provides implementations for both. This distinction maintains modularity and enables a multitude of useful combinations for analyzing spatial data with self‐organizing neural networks. Second, SPAWNN interactively links different self‐organizing networks and data visualizations in an intuitive manner to facilitate explorative data analysis. Third, it implements cutting‐edge clustering algorithms for identifying clusters in the trained networks. Toolkits such as SPAWNN are particularly needed when researchers and practitioners are confronted with large amounts of complex and high‐dimensional data. The computational performance of the implemented algorithms is empirically demonstrated using high‐dimensional synthetic data sets, while the practical functionality highlighting the distinctive features of the toolkit is illustrated with a case study using socioeconomic data of the city of Philadelphia, Pennsylvania.