A GIS Toolbox for Measuring and Mapping Person‐Based Space‐Time Accessibility

This article presents a toolbox to compute and map person‐based accessibility indicators, based on classical time geography concepts. The intent is to provide GIS and urban planning practitioners with a user‐friendly and easily customizable tool. While it relies on well‐known concepts, the toolbox implements a major innovation in person‐based accessibility assessment by taking into account opening hours when measuring the accessibility of urban facilities. The toolbox can be downloaded from http://bit.ly/1h6yg5Z .     

地图综合智能化研究的发展与思考

地图综合是地图制图和多尺度空间数据变换的核心与关键技术。20世纪60年代以来,数字地图数据的自动综合研究逐渐展开并取得了长足的进步,囿于人工智能技术的限制,地图综合的智能解决方法虽有不少成果,但距离真正的智能化、实用化仍有一定的距离。近年来,以深度学习为代表的人工智能技术应用于诸多研究领域并取得显著成效,地图综合的智能化研究也有诸多新的尝试。首先,在归纳自动地图综合研究模式的基础上,阐述了智能地图综合研究的必要性;然后,结合人工智能发展历程回顾智能地图综合研究,梳理和分析了基于传统机器学习与基于深度学习的智能地图综合研究现状,并归纳了地图综合智能化研究的主要方法;最后,围绕地图综合智能化研究中的几个热点问题,探讨了智能地图综合的发展趋势。     

Inset Mapper: A Software Tool In Island Cartography

Island cartography deals with special cartographic problems confronted in the portrayal of island regions and demands the use of specially developed software tools. One of the most commonly faced problems is the need of inset map creation for very small islands, and sometimes isolated ones, that must be displayed in the main map. This paper presents the methodology followed for the development of the Inset Mapper (IM) Software toolbox, describes the toolbox, and showcases its ability to create inset maps in Island regions. The IM software tool provides a useful cartographic tool for assisting the selection of the most appropriate position and scale of the inset map in an Island region.     

疫情防控期“数字地形测量学”线上教学实践

疫情之下,在线教学为“停课不停教、停课不停学”提供了理想的教学方案。基于超星泛雅网络教学平台,以“数字地形测量学”在线课程建设为例,探索在线课程的建设方法与途径,构建“能学、辅教”的立体化教学资源,进行“超星泛雅+学习通+腾讯课堂+课程QQ群”混合教学模式的探索和实践,为在线课程建设与应用提供借鉴。     

几种具有编解码结构的深度学习模型在建筑物综合中的应用与比较

深度学习技术促使诸多领域研究取得突破性进展, 基于深度神经网络的地图综合研究备受期待。将建筑物综合过程抽象解释为编解码过程, 系统地研究基于编解码结构的深度神经网络在建筑物综合中的应用。首先, 利用空间划分与矢量-栅格数据转换相结合的方式构建样本和样本集; 然后, 利用样本集训练基于编解码结构的深度神经网络, 实现建筑物综合学习泛化并测试、评估其效果; 最后, 搭建5种代表性的基于编解码结构的深度神经网络, 分析比较各模型在建筑物综合中的应用效果。实验结果表明, 基于编解码结构的深度神经网络能够从建筑物综合样本中学习或推理出部分建筑物综合知识和综合操作, 且5种模型中Pix2Pix更适用于建筑物综合的学习模拟。     

GIS&T pedagogies and instructional challenges in higher education: A survey of educators

Anyone involved in teaching the principles and applications of geographic information science and technology (GIS&T) understands the challenges of effective instruction within an environment subject to nearly constant change. Indeed, the dynamic nature of GIS&T introduces both new paradigms and increasingly powerful tools for exploring spatial relationships. However, while past efforts among educators and practitioners have identified knowledge and competencies important to GIS&T learning, less attention has been directed at methods used to teach GIS&T. For example, while some instructors employ traditional approaches such as lectures and structured laboratory exercises, others have shifted to active learning strategies such as “flipped classrooms” and collaborative, project‐oriented assignments. In this article, we assess the pedagogical approaches used to teach GIS&T courses through an Internet‐based survey of 318 college and university faculty. Our findings demonstrate that active learning pedagogies are becoming more firmly established, supplementing or replacing traditional teaching approaches. Contrary to our assumptions, age and teaching experience are not factors that influence the adoption of active learning strategies. Along with assessing instructional approaches, our survey identifies the challenges associated with teaching GIS&T, as identified by survey respondents.     

卓越计划背景下“测量程序设计”课程教学改革研究

在“卓越计划”背景下,针对“测量程序设计”课程,从优化教学内容、丰富教学手段、提高实践能力、完善考核体系等几个方面进行教学改革探讨,对其进行集“专业导向、课堂讲授、自主学习、课程实验”于一体化的全方位、多层次改革与实践,旨在实现该课程在测绘学科中的作用,全面提升学生分析和解决实际问题的能力,对培养卓越测绘工程师具有一定的参考价值。     

在特殊疫情背景下《城市地理资讯学》MOOC课程教学实施效果分析

在新冠肺炎疫情期间,线上教学大规模开展,相关线上教学问题逐渐成为研究热点.传统的测绘地理类课程的教学方式主要是上课时以老师传授知识为主,课后以学生完成作业为辅的方式进行.但是,这种教学方式比较单一,并且在科学化考核和促进学生学习主动性等方面存在诸多问题.然而,线上网络课程是通过互联网以电子设备为教学平台,具有不受教学时...     

基于语法层的地图综合信息量原则

地图自动综合的一个首要问题是对地图信息进行数量化,信息量正是对地图表达效率数量化的一种重要手段。本文讨论了地图综合在符号语言学上的层次结构,提出在"典型化"约束下,地图综合应遵循的信息量数量变化规律。该规律为地图自动综合结果的评价提供了数学基础,也能用作基于信息量的渐进式自动综合的评价函数。试验证明,该规律符合地图综合实践的结果。     

浅谈如何提高测绘工程专业学生对遥感原理与方法课程学习的兴趣

结合笔者在中南大学为期9年的遥感原理与方法课程教学实践,分析了中南大学测绘工程专业学生在该课程教学方面的学习态度和课程认识方面的现状,提出在教学过程初期加强学生对遥感历史文化及名人传记的学习,在课程教学期间自己查询专业应用前景及专业最新动态新闻,在上课期间主动向老师提问3种方法,以考核方式计入学生平时成绩,以期增强学生对该课程的学习兴趣和主动学习能力及独立思考能力,为后续遥感专业相关课程的学习奠定一定的基础。在近几年的教学实践过程中,发现该方法有效地提高了学生对该专业课的学习兴趣,学习效果超越往届学生,实践结果充分证明了该方法的有效性。     

高校GIS专业遥感类课程教学改革探索

阐述了遥感课程的特色及国内开设现状,对该课程的教学改革提出了一些建议。认为学习课程之前需要适当增加相关基础课程;课程体系需要进行优化,各门遥感课程要合理安排教学次序,选择合适的配套教材;注重补充学科前沿知识,扩充学生视野;教学方式需要以形象化教学为主,启发学生创造性思维,提高学生实践能力。     

USE OF SPACE PHOTOGRAPHS IN THE MAKING AND REVISION OF SMALL-SCALE MAPS

A brief review paper focuses on Soviet strategies for the use of space imagery, particularly photographic products (1:2,000,000 to 1:4,200,000 scale) from manned spaceflights, in the revision of small-scale general reference maps. Principles for the interpretation and mapping of basic map elements appearing on space photographs are outlined. Space photographs provide for reduction in time spent in map editing and the more correct analysis of the reliability of information and its generalization. A technology of map revision based on the joint use of original photographs and photomaps, with the transfer of revisions from photomaps to map originals, is proposed. Translated from: Geodeziya i kartografiya, 1986, No. 8, pp. 29-33.     

基于OpenMP的Li-Openshaw算法并行计算方法

随着网络地图、移动地图的发展,对地图自动综合效率的要求不断提高;并行计算技术已经在遥感图像处理、计算几何等方面显示出优势。本文尝试将地图综合算法与并行计算方法相结合,探讨地图综合算法效率提高的方法;并通过对Li-Openshaw线要素简化算法的分析,探讨了在多核处理器环境下该算法的并行计算策略,基于OpenMP实现了Li-Openshaw算法的并行计算,最后通过实验验证了基于OpenMP的Li-Openshaw线要素化简算法在效率上的提高。     

基于约束Delaunay三角网的线、面群目标分布边界计算

群组目标的分布边界在空间方向关系判断、相似度计算以及地图自动综合等领域有着重要的应用,但目前的分布边界计算主要是针对空间点群目标,鲜有涉及空间线、面群目标。在约束Delaunay三角网的基础上,利用动态阈值"剥皮"法实现线、面群目标的分布边界计算。方法得到的分布边界多边形符合人们的空间认知习惯,能够较好地描述线、面群目标的空间形态和分布范围。     

A New GIS-based Solar Radiation Model and Its Application to Photovoltaic Assessments

The solar radiation model r.sun is a flexible and efficient tool for the estimation of solar radiation for clear‐sky and overcast atmospheric conditions. In contrast to other models, r.sun considers all relevant input parameters as spatially distributed entities to enable computations for large areas with complex terrain. Conceptually the model is based on equations published in the European Solar Radiation Atlas (ESRA). The r.sun model was applied to estimate the solar potential for photovoltaic systems in Central and Eastern Europe. The overcast radiation was computed from clear‐sky values and a clear‐sky index. The raster map of the clear‐sky index was computed using a multivariate interpolation method to account for terrain effects, with interpolation parameters optimized using a cross‐validation technique. The incorporation of terrain data improved the radiation estimates in terms of the model's predictive error and the spatial pattern of the model outputs. Comparing the results of r.sun with the ESRA database demonstrates that integration of the solar radiation model and the spatial interpolation tools in a GIS can be especially helpful for data at higher resolutions and in regions with a lack of ground measurements.     

地图自动综合问题的分解和基本算子集合

从地图综合对象的本质特征出发,结合地图综合的自身规律,分解了地图自动综合问题。同时,在分析已有地图综合算子的基础上,提出了一套具有完备性的地图综合算子集合,讨论了算子的关联性和有序性。     

矢量曲线的视觉清晰度及在网络地图综合中的应用

互联网用户参与的地图制图容易出现视觉冲突、压盖、拥挤等地图表达问题,需要引入地图自动综合协助解决。网络地图中由于原图比例尺和综合后比例尺均难以准确量化,常规地图自动综合基于“原图比例尺-综合后比例尺”判断是否需要综合的方法已不再适用。矢量数据在可视化后会产生视觉粘连,视觉粘连越明显,地图表达效果越差,综合的需求也越强烈。基于此规律,本文提出对视觉粘连进行定量描述并据此判断是否需要综合。首先,从人类视觉感受出发,结合栅格化思想设计了矢量曲线视觉粘连的量化指标——视觉清晰度。然后,基于“金字塔式”的尺度空间计算曲线在多个比例尺表达的清晰度,并拟合了清晰度的变化函数。最后,将该函数应用于众源地理数据的网络地图综合决策。试验结果表明,本文方法可准确判断每条矢量曲线是否需要综合,能有效解决地理数据尺度异质性带来的可视化难题。同时,清晰度变化函数将曲线的尺度描述由静态数值扩展到连续函数,有望更好地支持多尺度空间数据处理及网络地图综合等问题。     

Integrating domain knowledge and graph convolutional neural networks to support river network selection

Deep learning is increasingly being used to improve the intelligence of map generalization. Vector-based map generalization, utilizing deep learning, is an important avenue for research. However, there are three questions: (1) transforming vector data into a deep learning data paradigm; (2) overcoming the limitation of the number of samples; and (3) determining whether existing knowledge can accelerate deep learning. To address these questions, taking river network selection as an example, this study presents a framework integrating hydrological knowledge into graph convolutional neural networks (GCNNs). This framework consists of the following steps: constructing a dual graph of river networks (DG_RN), extracting domain knowledge as node attributes of DG_RN, developing an architecture of GCNNs for the selection, and designing a fine-tuning rule to refine the GCNN results. Experiments show that our framework outperforms existing machine learning and traditional feature sorting methods using different datasets and achieves good morphological consistency after the selection. Furthermore, these results indicate that DG_RN meets the data paradigm of graph deep learning, and the framework integrating existing characteristics (i.e., Strahler coding, the number of tributaries, the distance between proximity rivers, and upstream drainage area) mitigates the dependence of GCNNs on plenty of samples and enhance its performance.     

面向模型的大比例尺制图综合框架设计与实现

模型综合作为制图综合的一部分 ,是通过改变地图内部目标关系的表达方式 ,使地图的表达更简单 ,进而使综合更方便。结合交互式制图综合软件AutoMap ,介绍了面向模型综合的系统设计框架和具体实现 ,并着重从地图目标的空间关系、几何属性和语义关系 3个方面来分析模型综合。     

多尺度变换中建筑群空间相似度计算模型

针对建筑群自动综合结果质量评价问题,该文提出一种建筑群多尺度表达中空间相似度计算模型。从地图综合质量评价的角度,立足地图综合的3个约束,即图形约束、拓扑约束、结构约束,分别计算了图形相似度、拓扑相似度、结构相似度,并对以上各相似度作了适当的权重分配,计算建筑群目标的整体相似度。最后,通过实验验证了本文相似度计算方法的正确性、可行性。