数字化线性地图要素的综合

本文从制图线理论出发,侧重讨论了数字化线性地图要素(链)的综合方法,并编写了与之相应的计算机自动综合程序,以用于机助制图。     

傅立叶级数支持下的等高线多尺度表达模型

基于傅立叶级数提取等高线的特征向量,并提出了一种等高线多尺度表达的数据模型。对开等高线采用以首尾点连线作镜像处理的方式将其拓展成闭合曲线,进而将闭合曲线描述成弧长的周期函数,并将其表示为傅立叶级数。以一定阶次的傅立叶级数展开曲线与原始曲线形成的缝隙面积为依据,计算当前展开曲线的比例尺,建立傅立叶级数展开项数与等高线比例尺的对应关系。不同于传统的通过对曲线上点和弯曲的取舍来实现尺度变化的方法,通过控制傅立叶特征向量的长度来实现不同尺度的等高线表达。     

Computer-aided design of bus route maps

The bus route map is a diagram that aims to convey necessary information for map readers to find an appropriate way of moving from an origin to a destination. Design of bus route map is a complicated and time-consuming task that requires careful consideration of readability and aesthetics. This paper proposes a new computational method for designing bus route maps. The method helps us to reduce six types of undesirable elements in bus route maps, i.e., gap, shift, crossing, overlap, misalignment, and acute bend. The method consists of two phases: line layout phase determines the relative order of bus routes on each road segment and map layout phase calculates the actual position of bus routes drawn on a map. This paper applies the method to the design of bus route maps of Chiba City, Japan. The result supports the effectiveness of the method as well as reveals open topics for future research.     

一种改进的基于转角函数法的面状居民地Morphing变换方法

提出了一种顾及位置偏差与凹部同异性的面状居民地Morphing变换转向角函数方法.该方法通过构造大比例尺居民地的凸包作为中间图形,辅助与小比例尺居民地进行特征顶点匹配,避免因位置偏差造成的特征点误匹配;针对大比例尺居民地的凹部,依据凹部的模式类型,判定凹部边的同异性;最后构建插值模型,得到中间任意尺度下的面状居民地要素...     

A hierarchical spatiotemporal adaptive fusion model using one image pair

ABSTRACT

Image fusion techniques that blend multi-sensor characteristics to generate synthetic data with fine resolutions have generated great interest within the remote sensing community. Over the past decade, although many advances have been made in the spatiotemporal fusion models, there still remain several shortcomings in existing methods. In this article, a hierarchical spatiotemporal adaptive fusion model (HSTAFM) is proposed for producing daily synthetic fine-resolution fusions. The suggested model uses only one prior or posterior image pair, especially with the aim being to predict arbitrary temporal changes. The proposed model is implemented in two stages. First, the coarse-resolution image is enhanced through super-resolution based on sparse representation; second, a pre-selection of temporal change is performed. It then adopts a two-level strategy to select similar pixels, and blends multi-sensor features adaptively to generate the final synthetic data. The results of tests using both simulated and actual observed data show that the model can accurately capture both seasonal phenology change and land-cover-type change. Comparisons between HSTAFM and other developed models also demonstrate our proposed model produces consistently lower biases.     

Exploring the reasons for the seasons using Google Earth, 3D models,and plots

ABSTRACT

Public understanding of climate and climate change is of broad societal importance. However, misconceptions regarding reasons for the seasons abound amongst students, teachers, and the public, many of whom believe that seasonality is caused by large variations in Earth’s distance from the Sun. Misconceptions may be reinforced by textbook illustrations that exaggerate eccentricity or show an inclined view of Earth’s near-circular orbit. Textbook explanations that omit multiple factors influencing seasons, that do not mesh with students’ experiences, or that are erroneous, hinder scientifically valid reasoning. Studies show that many teachers share their students’ misconceptions, and even when they understand basic concepts, teachers may fail to appreciate the range of factors contributing to seasonal change, or their relative importance. We have therefore developed a learning resource using Google Earth, a virtual globe with other useful, weather- and climate-related visualizations. A classroom test of 27 undergraduates in a public research university showed that 15 improved their test scores after the Google Earth-based laboratory class, whereas 5 disimproved. Mean correct answers rose from 4.7/10 to 6/10, giving a paired t-test value of 0.21. After using Google Earth, students are helped to segue to a heliocentric view.     

基于改进K-SVD字典学习方法的地震数据去噪

为实现更好的地震数据去噪技术,笔者引入一种新的算法:快速迭代收缩阀值法(FISTA),通过FISTA和K-奇异值分解(K-SVD)不断迭代更新K-SVD字典,利用更新得到的K-SVD字典对地震数据进行稀疏表示,去除稀疏系数中较小的数值,使数据中的随机噪声得到压制。对层状模型合成地震记录,Marmousi模型合成地震记录以及实际地震数据进行对比实验,得出FISTA算法较OMP算法能更好地提高地震数据的信噪比,同时有效地保护了反射信号。     

Placing of photos on the internet: Critical analysis of biases on the depictions of France and Afghanistan on FLICKR

The Internet and the representation of space therein are almost omnipresent in society and everyday life. Peer-produced geographic data is gaining a particular importance through increasingly available digital tools and techniques that shape the perception of space in the internet, such as flickr, OpenStreetMap or Wikipedia. However, few studies focused on how space is represented, and by whom it is described. We hypothesize that the alleged opening up of geographic information and the assumed benefits for every individual and society through the occurrence of ‘easy-to-use-mapping-tools’ was premature. To explore these assumptions, a comparative study of the flickr worldmap was undertaken and roughly 6.8 million metadatasets of geocoded photos in France, and roughly 50,000 metadatasets in Afghanistan were downloaded and the metadata was analyzed. Our results indicate that photos geocoded in France show a large diversity of motives, while photos geocoded in Afghanistan are mostly limited to content containing warfare when they are up loaded in English. The content of the photo and therefore the representation of space strongly depend on who uploaded the photo, particularly in Afghanistan. We can show that the representation of space on the internet, for the case of flickr, is strongly dominated by perceptions of Western societies and individuals. We therefore confirm our hypothesis that the supposed opening up of geographic information systems through ‘easy-to-use-mapping-tools’ and their democratization thereof was premature. Moreover, we highlight the importance of understanding who contributes online content to be able to evaluate peer-produced data, its value, and its possible applications to avoid reproducing biases.