Hill-shading, also referred to as relief shading, is an important visualization method in small- and medium-scale topographic mapping. Although analytical hill-shading methods offer high generation efficiency, they often suffer from terrain detail overload. Some deep learning generative models have introduced terrain priors, but they still have deficiencies in the controllable representation and reasonable simplification of terrain structures. This study proposes a Swiss-style hill-shading method based on hierarchical terrain structure lines. By introducing ridgelines with hierarchical semantics as priors, a synergistic regulation mechanism of enhancement and suppression is constructed to emphasize major terrain structures while moderately weakening secondary details. The model adopts a hybrid architecture integrating a Transformer encoder and a convolutional decoder to model global terrain structures. In the testing phase, by adjusting the combinations of different levels of ridgelines, the controllable adjustment of the level of detail and structural significance in hill-shading representation is realized. Multi-region experimental results show that the proposed method can generate hill-shading results with clear structural hierarchies and distinct primary–secondary relationships. Compared with other models, the proposed method demonstrates more stable performance in achieving reasonable hierarchical representation of terrain structures and detail simplification, approaching the visual style of Swiss-style manual hill-shading.

The compelling visualization of Digital Elevation Models (DEMs) constitutes a vital intersection between Geographic Information Science (GIS) and the digital humanities. Nevertheless, traditional Generative Adversarial Networks (GANs) frequently demonstrate a “geography-blind” characteristic, resulting in structural “topographic drift” by dissociating geomorphic complexity from cartographic constraints. To overcome this limitation, we propose Geo-InkGAN, a geo-heuristic framework that integrates geographic principles with generative processes to achieve high-fidelity ink-wash style synthesis. A key component of our approach is an adaptive optimization strategy grounded in the Slope Standard Deviation (SSD). By establishing a quantitative relationship between geomorphological entropy and the cycle-consistency loss weight (λcyc), we effectively address the Pareto trade-off between geomorphic accuracy and esthetic representation. Our results indicate that alluvial plains benefit from low-intensity constraints to facilitate fluid ink diffusion, whereas rugged terrains require high-intensity constraints to maintain the integrity of the topological framework. Additionally, the HCEG-SE mechanism (Hillshade-Contour Edge-Guided Stroke Enhancement) narrows the semantic divide between terrain skeletons and artistic textures by combining multi-directional non-photorealistic rendering with precise edge extraction techniques. Evaluated across five geomorphologically diverse regions—from karst towers to loess plateaus—Geo-InkGAN demonstrably surpasses existing benchmarks in Geomorphological Structure Correlation (GSC). This geomorphology-aware approach advances the scientific rigor of AI-driven cartography and offers a refined methodology for the cultural representation of digital twin landscapes.

Getting lost in complex urban environments is common, yet the environmental determinants of the risk of getting lost (RGL) remain poorly quantified. This study develops a multi-scale, data-driven framework that links local visual-perceptual attributes (e.g., sky visibility, scene openness, pedestrian density) with global spatial-structural metrics (e.g., road curvature, road type, land-use pattern) derived from multi-source geospatial data and image semantic segmentation. Using 3303 easy-to-get-lost (E2G) locations and 3303 matched easy-to-navigate (E2N) locations across six urban context types, we employ random forest regression to identify key environmental correlates of RGL. Results reveal strong context dependence alongside consistent cross-cutting mechanisms. In tourist areas, RGL is primarily driven by road curvature and mitigated by higher sky visibility. In transportation hubs, road type, curvature, and building density elevate RGL, whereas clearer guidance signage and more concentrated land use have protective effects. In cultural and business districts, pedestrian density is the dominant driver, with additional amplification from complex road geometry and fine-grained functional mixing. In residential areas, higher sky visibility and scene openness systematically reduce RGL, while greater building density and road curvature increase it. Across contexts, open, legible vistas are generally associated with lower RGL, whereas crowding and path complexity increase disorientation. The proposed framework achieves a peak predictive accuracy of 0.759 in transportation hubs. Although non-causal, these relationships provide an actionable evidence base for embedding wayfinding legibility into transport and urban design, emphasizing visual openness, simplified layouts, demand management, and standardized guidance to support more navigable and sustainable cities.

Map emotional expression refers to the process of endowing maps with emotional qualities through visual design. Emotional expression consistent with the map context can enhance the user's emotional engagement, while the potential of map emotional expression remains insufficiently explored. Color plays a central role in facilitating emotional communication with map users, appropriate color style design has the potential to improve map's emotional expressiveness. In this study, a map color style design model is proposed to support emotion-oriented color style design and enhance map emotional expression. The model introduces a Map Emotion Dataset for extracting emotional color manifold features, and constructs emotional coloring constraint functions to effectively guide the iterative process to generate map color styles that better align with target emotion. The proposed model offers a framework for enhancing the emotional expressiveness of maps through color style design. The perception evaluation results show that the color styles generated by the proposed model exhibit more stable performance than CycleGAN and manual design in different emotion categories and are generally well accepted by readers. Furthermore, in the evaluation of visual quality, the method demonstrates good performance in scene appropriateness, hierarchical clarity, and color rationality.

Relief shading is a primary technique for representing the three-dimensional effects of terrain on a two-dimensional plane. This study applies deep learning to generate small-scale Swiss-style relief shading maps. An attention module is defined to focus on key information in feature maps. Based on the characteristics of relief shading and digital elevation model (DEM) data, U-Net is adjusted and optimized, resulting in the design and construction of an end-to-end relief shading neural network model (Attention Hillshading U-Net, A-UNet) built on a limited training dataset. By learning the terrain-shaping patterns from Swiss-style shading maps, the model overcomes the challenges posed by high terrain complexity and insufficient representation of landform morphology in small-scale relief shading maps. The study further investigates the impact of hyperparameters on the performance of the model in generating small-scale relief shading maps. Based on the quantitative performance of the model under different hyperparameter settings and adaptability to lower-resolution DEMs, the optimal hyperparameters for the model are determined. Additionally, experimental comparisons of small-scale relief shading map generation using A-UNet and other network models show that, compared to U-Net and its variants, A-UNet demonstrates superior adaptability to different pixel sizes, better terrain simplification, and enhanced generalization to various landform types.

This study selected the Sino-US route data from the top 30 global container liner companies between December 1, 2019, and December 29, 2019, as the data source utilizing the complex network research methodology. It constructs a Sino-US container shipping network through voyage weighting and analyzes the essential structural characteristics to explore the network’s complex structural features. The network’s evolution is examined from three perspectives, namely, time, space, and event influence, aiming to comprehensively explore the network’s evolution mechanism. The results revealed that: 1) the weighted Sino-US container shipping network exhibits small-world and scale-free properties. Key hub ports in the United States include NEW YORK NY, SAVANNAH GA, LOS ANGELES CA, and OAKLAND CA, whereas SHANGHAI serving as the hub port in China. The geographical distribution of these hub ports is uneven. 2) Concerning the evolution of the weighted Sino-US container shipping network, from a temporal perspective, the evolution of the regional structure of the entire Sino-US region and the Inland United States is in a stage of radiative expansion and development, with a need for further enhancement in competitiveness and development speed. The evolution of the regional structure of southern China and Europe is transitioning from the stage of radiative expansion and development to an advanced equilibrium stage. The shipping development in Northern China, the Western and Eastern United States, and Asia is undergoing significant changes but faces challenges of fierce competition and imbalances. From a spatial perspective, the rationality and effectiveness of the improved weighted Barrat-Barthelemy-Vespignani (BBV) model are confirmed through theoretical derivation. The applicability of the improved evolution model is verified by simulating the evolution of the weighted Sino-US container shipping network. From an event impact perspective, the Corona Virus Disease 2019 (COVID-19) pandemic has not fundamentally affected the spatial pattern of the weighted Sino-US container shipping network but has significantly impacted the network’s connectivity. The network lacks sufficient resilience and stability in emergency situations. 3) Based on the analysis of the structural characteristics and evolution of the weighted Sino-US container shipping network, recommendations for network development are proposed from three aspects: emphasizing the development of hub ports, focusing on the balanced development of the network, and optimizing the layout of Chinese ports.

The main purpose of the research on map emotional semantics is to describe and express the emotional responses caused by people observing images through computer technology. Nowadays, map application scenarios tend to be diversified, and the increasing demand for emotional information of map users bring new challenges for cartography. However, the lack of evaluation of emotions in the traditional map drawing process makes it difficult for the resulting maps to reach emotional resonance with map users. The core of solving this problem is to quantify the emotional semantics of maps, it can help mapmakers to better understand map emotions and improve user satisfaction. This paper aims to perform the quantification of map emotional semantics by applying transfer learning methods and the efficient computational power of convolutional neural networks (CNN) to establish the correspondence between visual features and emotions. The main contributions of this paper are as follows: (1) a Map Sentiment Dataset containing five discrete emotion categories; (2) three different CNNs (VGG16, VGG19, and InceptionV3) are applied for map sentiment classification task and evaluated by accuracy performance; (3) six different parameter combinations to conduct experiments that would determine the best combination of learning rate and batch size; and (4) the analysis of visual variables that affect the sentiment of a map according to the chart and visualization results. The experimental results reveal that the proposed method has good accuracy performance (around 88%) and that the emotional semantics of maps have some general rules.

The extraction of ship behavior patterns from Automatic Identification System (AIS) data and the subsequent prediction of travel routes play crucial roles in mitigating the risk of ship accidents. This study focuses on the Wuhan section of the dendritic river system in the middle reaches of the Yangtze River and the partial reticulated river system in the northern part of the Zhejiang Province as its primary investigation areas. Considering the structure and attributes of AIS data, we introduce a novel algorithm known as the Combination of DBSCAN and DTW (CDDTW) to identify regional navigation characteristics of ships. Subsequently, we develop a real-time ship trajectory prediction model (RSTPM) to facilitate real-time ship trajectory predictions. Experimental tests on two distinct types of river sections are conducted to assess the model’s reliability. The results indicate that the RSTPM exhibits superior prediction accuracy when compared to conventional trajectory prediction models, achieving an approximate 20 m prediction accuracy for ship trajectories on inland waterways. This showcases the advancements made by this model.

在标签地图应用日益增加的背景下,亟需开展标签权重表达策略的评估研究.引入眼动跟踪方法,针对常用于标签地图权重表达的一种等差字大策略进行评估.实验设定无目的自由浏览和有目的阅读分析两个应用场景,布置标签选取、识别/搜索、记忆和主观评价任务,统计分析被试完成以上任务的眼动数据和其他衍生数据,结果显示:(1)不同大小的标签在信息凸显性、视觉吸引力、权重记忆以及识别/搜索时的搜索效率、阅读效率和认知负担方面并未表现出明显差异性;(2)文字大小处于上游的标签相比处于下游的标签更加容易被识别/搜索,被试的兴趣度更高,但并不意味着文字越大的标签越容易被识别/搜索,被试的兴趣度越高;(3)采用等差字大策略的标签地图总体评价良好.该研究有助于地图设计者进一步了解等差字大策略的特点.

第二次全国地名普查（以下简称"二普"）数据具有精度高、类型全面、现势性强等特点。如何对地名数据进行最优符号化是"二普"成果转化中一个重要的问题。为此,本文构建了地图符号库树结构模型,提出从主题尺度、符号形态、要素分类分级3个方面进行符号视觉层次构建的思路,充分利用视觉变量构图元素的组合实现多层次符号视觉效果。将该方法运用于《汕头市澄海区地名图集》的编制中,通过符号设计实例充分论证符号视觉层次体系设计在地名图集设计中的重要作用,可为"二普"成果转化工作中的地名图编制提供参考。

野外实践教学是测绘地理信息类专业人才培养的重要内容.本文详细分析了国内外测绘地理信息类专业野外实践教学现状,构建了三峡秭归基地测绘地理信息类专业多层次、多维度、立体化及数字化野外实践教学体系,开发了包括人文地理、自然地理、基础地质和数字测绘等野外实习资源,提出了点线面结合方法、现代高新技术方法和移动APP野外教学法等多种野外实习方法.实践结果表明,所构建的野外实践体系能够极大地提高学生野外实践能力,增强了学生对理论知识的理解.

The extraction of ridge lines from digital elevation models Digital elevation models is fundamental for generalizing digital elevation models (DEMDigital elevation models (DEM) ), analyzing digital valleys, remote sensing analysis, and topographic indices analysis. In this paper, the authors propose a method to extract ridge linesRidge lines from a gridded DEM called the Steepest Ascent MethodSteepest ascent method Based on Constrained Direction (SAMBCD).SAMBCD In the SAMBCD method, based on the overland flow simulation Overland flow simulation method and the steepest ascent method, the authors define control points and constrain the direction of connecting points in the process of organizing major ridge lines and minor ridge lines, respectively, among the discrete points of the catchment boundary. Specifically, with SAMBCD, the points on hill peaks and saddle points are first sorted and used as control points to constrain the connecting direction. Second, the major ridge lines are organized by connecting the relevant discrete points of the catchment boundary, following the constrained direction. Finally, the remaining discrete points of the catchment boundary are connected to form the minor ridge lines with the same constrained connecting direction. Many tests are performed to extract ridge lines in different regions. The results show that the method of SAMBCD is effective for decreasing the fracture, cross and rotation of extracted ridge lines. Moreover, SAMBCD has better performance in the continuity and integrity of ridge lines than the two basic methods mentioned above.

The study develops a modeling framework to quantify the saliency of individual buildings and to visualize them accordingly in a virtual 3D urban environment for wayfinding. Based on the cognitive characteristics of wayfinding behaviors and considering the relationships among pathfinder, buildings, and local context of the urban environment, this study defines and distinguishes two types of variables related to the saliency of a building, the global salience variables and the local salience variables. Then an adaptive weight distribution method is developed to determine the weight for each variable. Global salience is estimated in the context of a broader urban space, while local salience is estimated in the local region. Finally, a synthesized salience model is constructed by integrating the global and local salience values with consideration of the visibility factor. The salience values are used to identify most salient landmarks in each scene on the path of wayfinding. In the implemented prototype, 3D visualization is made for each scene showing buildings with varying levels of details according to their salience values. This study contributes to the research on intelligent 3D navigation systems by providing effective visual guidance for wayfinding tasks in virtual urban environments.

Based on the basic theory of 3D expression of geographic elements and the concept, research framework and logic component of 3D map, the dynamic extension of Bergins visual variables in 3D visualization and the designing of 3D cartographic symbol are studied, to systematically analyze the 3D symbolic expression of geographic elements considering the terrain at different scales in 3D space by referring to the traditional cartographic expression theories and combining the characteristics of 3D environment. Then, the levels of detail in 3D point-shaped symbols and the deal of the relationship between point-shaped symbols and terrain model or other elements are discussed in the process of studying expression of point-shaped geographic elements in 3D space. Besides, the line-shaped elements are categorized into three types of elements, which are single-river, boundary and road, to study their symbolized expression based on terrain model in 3D environment respectively, and the multiple expression approach of the road at different scales is discussed emphatically in this part. Finally, the 3D expression of vegetation, soil and water treated as area elements is discussed respectively, according to geographic meaning and 3D modeling approaches of cartographic elements.

In this paper the basic concept and technical characteristics of airborne 3D digital earth are expounded at first, and then the functions and running mode are analyzed, and the organization and optimization strategies of multi-resolution global terrain and image models and aviation electronic map are studied in VxWorks. Furthermore, the author explores such key technologies as dynamic data scheduling, progressive graphics refreshing, rendering in real-time and map symbolization of each data tile in 3D scene, analysis and computing of ground proximity warning, aviation information displaying, etc. It has been proved that these technologies are very effective in the actual application of embedded 3D digital earth system and have broad prospects.

设计并实现一个基于GIS组件ArcGIS Engine,采用Oracle 10g数据库并与ArcGIS空间数据引擎(SDE)相结合,以VisualC++为开发平台,以OpenGL为三维图形引擎的港口与航道信息管理系统,包括水深数据库管理、地图及水深数据调阅与查询、三维港口景观与水下地形漫游、三维航道分析4个模块;阐述该系统的体系结构和主要功能,给出系统的工作流程,并对若干模块的设计进行深入探讨。目前,该系统已用于天津港水深数据与航道信息的管理。

3D visualization technology is widely used in science research, engineering design, management and decision in port, shipping, channel and coast engineering, water transportation, hydrology and its other related spheres. Firstly, this paper introduces the 3D Harbor Area and Waterway Information Manage System of Tianjin based on this technology, and expounds the modeling and integration of large-scale scenes comprised of port landscapes, mooring machineries and underwater terrain models are expounded. Meanwhile, the author emphatically studies the dynamic organization and management methods of landscapes and digital terrain models supported by these technologies, such as spatial grid index, Delaunay triangulation and LOD (Level of Detail). And discusses the real-time reading and rendering of subscenes and thematic data based on multi-thread and display list of OpenGL. In addition, dynamic ocean, panoramic sky, underwater terrain simulation and navigation, interactive operation and information query are also investigated. It is practiced that the rendering performance is still perfect under the conditions of massive data and complex scenes in the 3D visualization system developed by the author, which has fluent image and high fidelity, and meets the need of 3D visualization and spatial analysis of waterway, port and related apartment.

三维可视化理论与技术的引入使得传统静态的、平面的地图向动态的、三维的方向发展,地图的三维可视化正成为GIS与数字制图领域的一个新的研究课题.数字地图及其建库技术的研究为其三维可视化研究提供了有力的技术支持和充足的数据来源.主要研究基于等高线与离散高程点的地形模型(DEM)的建立、兼顾地形(即基于DEM)的街区与街道、居民地模型的建立,以及植被、道路、水系等各主要要素的三维模型的建立及其显示,对三维点状地图符号库的建立、模型的动态显示及航空影像的叠加显示等进行了研究.最后对三维可视化技术在基于数字地图的地貌晕渲、模拟飞行、虚拟城市等方面的应用作了探讨.

在世纪之交,随着计算机科学的迅速发展,多媒体教学系统已逐渐被人们认识而受到各个高校的喜爱.教育现代代无疑是教学方式、教学手段现代化,多媒体教学已经成为当今教学的一种新时尚和重要手段.为此,我们制作了<地图概论>多媒体教学软件,供地理信息系统专业的学生学习时使用.