An improved dark object method to retrieve 500 m-resolution AOT (Aerosol Optical Thickness) image from MODIS data: A case study in the Pearl River Delta area,China

This paper presents an improved Dark Dense Vegetation (DDV) method for retrieving 500 m-resolution aerosol optical depth (AOT) based on MOD04-C005 arithmetic with the Moderate Resolution Imaging Spectroradiometer (MODIS) from the National Aeronautics and Space Administration (NASA). The improvements include change of the movement pattern of retrieval window, selection of a more suitable aerosol type, and storage of the look-up table. The method is then applied to obtain the AOT over the Pearl River Delta region (PRD). By comparing the results with the co-temporal ground sunphotometer observations in 2010, the correlation coefficient is found to be 0.794 with RMSE 0.139 and their variations remain consistent. Contrasts between model values in 2008 and MODIS AOT products in the same date also reveal a high accuracy of the improved DDV method. We also performed sensitivity tests to analyze the impacts of several parameters on apparent reflectance at different bands, and the results show that apparent reflectance is much more sensitive to surface reflectance and AOT than to elevation.     

Multi-class geospatial object detection and geographic image classification based on collection of part detectors

The rapid development of remote sensing technology has facilitated us the acquisition of remote sensing images with higher and higher spatial resolution, but how to automatically understand the image contents is still a big challenge. In this paper, we develop a practical and rotation-invariant framework for multi-class geospatial object detection and geographic image classification based on collection of part detectors (COPD). The COPD is composed of a set of representative and discriminative part detectors, where each part detector is a linear support vector machine (SVM) classifier used for the detection of objects or recurring spatial patterns within a certain range of orientation. Specifically, when performing multi-class geospatial object detection, we learn a set of seed-based part detectors where each part detector corresponds to a particular viewpoint of an object class, so the collection of them provides a solution for rotation-invariant detection of multi-class objects. When performing geographic image classification, we utilize a large number of pre-trained part detectors to discovery distinctive visual parts from images and use them as attributes to represent the images. Comprehensive evaluations on two remote sensing image databases and comparisons with some state-of-the-art approaches demonstrate the effectiveness and superiority of the developed framework.     

面向户外增强现实的地理实体目标检测

本文研究了多种计算机视觉领域中用于图像分类和目标检测的卷积神经网络模型,在一种目标检测模型的基础上进行了修改,并结合了残差神经网络结构,旨在解决户外增强现实中地理实体目标检测的问题。我们根据这一模型开发了一款户外增强现实原型系统,通过将训练好的模型作为目标检测引擎,达到了较好的近实时性和准确性。     

基于光谱整体形态和局部吸收谷位置的高光谱遥感图像地物识别

为了精确识别高光谱遥感图像上的地物,使用交叉相关光谱匹配方法获取光谱整体形态的最大偏移量,通过对比分析吸收谷位置变化获取吸收谷位置的最大偏移量,在此基础上确定吸收谷位置的偏移范围;然后使用穷举法,进行基于整体形态和偏移范围内局部吸收谷位置的识别。在云南中甸普朗斑岩铜矿区的高光谱遥感图像的实际应用表明,该方法的识别结果比仅考虑整体形态的识别结果具有更高的可靠性,能进一步证实识别结果的准确性。     

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.     

一种基于空间对象的缓冲区分析算法

在传统缓冲区分析基本思想的基础上,提出基于空间对象缓冲区分析定义,区别在于邻域半径:前者为常量,后者为变量。设计实现基于空间对象缓冲区分析算法,该算法以空间对象为计算粒度,分为计算缓冲区边界点、生成缓冲区多边形、筛选缓冲区多边形内空间对象3个步骤,并从算法执行机理的角度对其做定性与定量评估。结果表明,该算法解决了传统缓冲区分析难以处理图层内以空间实体为分析粒度的问题,时间和空间复杂度亦优于传统缓冲区分析算法。     

基于关系数据库的邻接域处理

本文就跨图幅的地物(邻接域)的管理问题作了一些有益的探讨，为了达到有效管理这个目的，本文采用Oracle公司提供的空间数据插件Spatial将空间数据和属性数据存入Oracle数据库中，这样不仅可以通过标准SQL语句可下裁任意区域的数据，而且数据库还会自动维护数据的完整性。因此，用空间数据库的管理方式使邻接域问题就不存在了。     

面向对象的城市管网数据组织模型设计

针对现有城市管网数据组织方式存在的问题,提出了一种基于面向对象的城市管网数据组织模型,该模型从人们认知的角度,将客观世界中的管网作为集数据和方法于一体的对象综合体,进行一体化存储和管理。模型在引入"类"概念的基础上,对基类中几何类和实体类的结构进行了详细设计,并讨论了模型中类与类、类与对象之间的关系,最后以城市供水管网为例,设计了基于对象的城市供水管网数据组织模型。     

Applying VGI to collaborative research in the humanities: the case of ARTL@S

ABSTRACT

Using Artl@s as an example of a project that relies on volunteered geographic information (VGI), this article examines the specific challenges that exist, beyond those frequently discussed in general VGI systems (e.g., participants’ motivation and data quality control) in regard to sharing research data in humanities: (1) most data from the humanities is qualitative and collected from multiple data sources which are often inconsistent and unmappable; (2) data is usually interconnected with multiple relationships among different tables which creates challenges for both mapping and query functionality; (3) data is both geographical and historical. Consequently addresses that no longer exist have to be geolocated and visualized on historical basemaps and spaces must be represented diachronically; (4) the design of web map application needs to balance both sophisticated research requirements and a user-friendly interface; (5) finally contributors expect their data to be cited or acknowledged when used in other studies and users need metadata and citation information in order to reuse and repurpose datasets.

In this article, we discuss how Artl@s, a project which developed a georeferenced historical database of exhibition catalogues, addresses these challenges. Artl@s provides a case study for VGI adoption by digital humanities scholars for research data sharing, as it offers features, such as flexible batch data contribution, interrelated spatial query, automatic geolocalization of historical addresses, and data citation mechanisms.     

面向对象的图像分类方法与彩色扫描地形图林地层的自动提取

提出了一种基于面向对象分类方法的彩色扫描地形图林地层自动识别方法，简要阐述了这种方法的基本概念和原理，分析和讨论了该方法的优越性和林地层提取效果，为基于地形图计算林地面积，找林地动态变化或土地利用类型变化的研究提供了方法参考。