A New Algorithms of Stroke Generation Considering Geometric and Structural Properties of Road Network

Liu, Yi; Li, Wenjing

doi:10.3390/ijgi8070304

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…Among the problems of cartographic generalizations based on winding mountain roads, deep-learning algorithms of neural networks are also used to solve them [25]. These are the algorithms that are used by scientists to generalize and smooth out winding roads [26]. Despite the errors in the results of the algorithms' work (which attracted the attention of the authors of this algorithm), artificial neural networks have attracted attention as a tool of the future.…”

Section: Literaturementioning

confidence: 99%

Digital Cartographic Generalization – Study of Its Thresholds and Stages in Example of Cartographic Line

Budkowski

2023

GaEE

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Digital generalization of spatial data has been the goal of the research in many research centers around the world. This article presents the evolution of cartographic generalization, drawing the reader’s attention to the change of its nature from analog to digital. Despite the passage of time and developing technologies, scientists have unfortunately yet to develop a uniform automatic generalization algorithm. One of the factors that hinder this process is the high complexity and complication of the whole process. The article is an attempt to answer this problem and addresses the issue of digital cartographic generalization by creating a proposal of thresholds and stages of cartographic generalization depending on the ratios of the numbers of points of generalized objects. The publication attempts to examine the possibility of applying an objective criterion of drawing recognition by examining digital generalization algorithms and setting its thresholds. The practical aim of the publication is to present generalization thresholds on the example of Chrobak’s algorithm. The proposal to make the selection of generalization thresholds dependent on the percentage share of points is a solution that is as simple to use as it is to implement. The method of defining intervals based on the three-sigma rule is a solution that guarantees that the obtained results will be characteristic of the probability density function of the normal distribution, which will define individual intervals most objectively.

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Section: Literaturementioning

confidence: 99%

Digital Cartographic Generalization – Study of Its Thresholds and Stages in Example of Cartographic Line

Budkowski

2023

GaEE

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“…These methods primarily focus on the overall morphology of the road network using geometric and topological information. Among them, common improvement methods are based on the stroke-based approach [16]. Some scholars [17] have fused three algorithms (a stroke-based, a grid-based, and a combined stroke-mesh algorithm) with three selection methods and made corresponding improvements.…”

Section: Introductionmentioning

confidence: 99%

Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network

Zheng,

Zhang,

et al. 2024

Preprint

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Selecting road networks in cartographic generalization has consistently posed formidable challenges, driving research towards the application of intelligent models. Despite previous efforts, the accuracy and connectivity preservation in these studies, particularly when dealing with road types of similar sample sizes, still warrant improvement. To address these shortcomings, we introduce a Heterogeneous Graph Attention Network (HAN) for road selection, where feature masking method is initially utilized to assess the significance of road features. Concentrating on the most relevant features, two meta-paths are introduced within the HAN framework: one for aggregating features of the same road type within the first-order neighborhood, emphasizing local connectivity, and another extending this aggregation to the second-order neighborhood, capturing a broader spatial context. For a comprehensive evaluation, we introduce a set of metrics considering both quantitative and qualitative aspects of the road network. Compared to traditional neural networks, our HAN model demonstrates superior accuracy and F1-score, particularly for road types with comparable samples. Additionally, it enhances the overall connectivity of the selected network. In summary, our HAN-based method provides an advanced solution for road network selection, surpassing previous approaches in terms of accuracy and connectivity preservation.

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“…National Mapping Agencies (NMA) are involved in the process of map generalisation, also for the purpose of seeking financial gains. Scientists agree on the need for complete automation of the generalisation process (among others, Burghardt et al, 2008;Chaundhry & Mackaness, 2008;Kilpeläinen & Sarjakoski, 1995;Liu & Li, 2019;Regnauld, 2015;Stoter et al, 2009aStoter et al, , 2009bStoter et al, , 2016Weibel, 1995;Weiss & Weibel, 2014).…”

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confidence: 99%

Automatic simplification of the geometry of a cartographic line using contractive self-mapping – illustrated with an example of a polyline band

Bac-Bronowicz,

Banasik,

Chrobak

2023

Polish Cartographical Review

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The present article is another attempt to adapt map geometry to automatic digital cartography. The paper presents a method of digital polyline generalisation that uses contractive self-mapping. It is a method of simplification, not just an algorithm for simplification. This method in its 1996 version obtained a patent entitled “Method of Eliminating Points in the Process of Numerical Cartographic Generalisation” – Patent Office of the Republic of Poland, No. 181014, 1996. The first results of research conducted using the presented method, with clearly defined data (without singular points of their geometry), were published in the works of the authors in 2021 and 2022. This article presents a transition from the DLM (Digital Landscape Model) to the DCM (Digital Cartographic Model). It demonstrates an algorithm with independent solutions for the band axis and both its edges. The presented example was performed for the so-called polyline band, which can represent real topographic linear objects such as rivers and boundaries of closed areas (buildings, lakes, etc.). An unambiguous representation of both edges of the band is its axis, represented in DLM, which can be simplified to any scale. A direct consequence of this simplification is the shape of the band representing the actual shape of both edges of the object that is classified in the database as a linear object in DCM. The article presents an example performed for the so-called polyline band, which represents real topographic linear objects (roads, rivers) and area boundaries. The proposed method fulfils the following conditions: the Lipschitz condition, the Cauchy condition, the Banach theorem, and the Salichtchev’s standard for object recognition on the map. The presented method is objective in contrast to the previously used approximate methods, such as generalisations that use graph theory and fractal geometry, line smoothing and simplification algorithms, statistical methods with classification of object attributes, artificial intelligence, etc. The presented method for changing the geometry of objects by any scale of the map is 100% automatic, repeatable, and objective; that is, it does not require a cartographer’s intervention.

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A New Algorithms of Stroke Generation Considering Geometric and Structural Properties of Road Network

Cited by 10 publications

References 14 publications

Digital Cartographic Generalization – Study of Its Thresholds and Stages in Example of Cartographic Line

Digital Cartographic Generalization – Study of Its Thresholds and Stages in Example of Cartographic Line

Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network

Automatic simplification of the geometry of a cartographic line using contractive self-mapping – illustrated with an example of a polyline band

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