A deep learning approach for polyline and building simplification based on graph autoencoder with flexible constraints

Yan, Xiongfeng; Yang, Min

doi:10.1080/15230406.2023.2218106

Cited by 5 publications

(2 citation statements)

References 58 publications

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“…Finally, through an analysis of the clusters, key nodes, and highly cited articles, we found that key technologies such as 'Artificial Neural Network' [78,89,92], 'Self Organizing Map' [87,[93][94][95][96][97][98], 'Back Propagation Neural Network' [93,[99][100][101], 'Particle Swarm Optimization' [102], 'Radial Basis Function Networks' [102], 'Convolutional Neural Network' [81,90,101,[103][104][105][106][107][108], 'Graph Neural Network' [88,[109][110][111][112][113][114], 'Support Vector Machine' [79,99], etc., are widely applied in various domains of cartography. Firstly, these technologies have automated cartographic workflows in map generalization [104,115], including polyline simplification [78,92,114,115], river network generalization [87,98,99], selective omission of road networks [79,…”

Section: Cluster #1 and #3-deep Learning And Machine Learningmentioning

confidence: 99%

Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace

Cheng,

Zhang,

Wang

et al. 2024

IJGI

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Propelled by emerging technologies such as artificial intelligence and deep learning, the essence and scope of cartography have significantly expanded. The rapid progress in neuroscience has raised high expectations for related disciplines, furnishing theoretical support for revealing and deepening the essence of maps. In this study, CiteSpace was used to examine the confluence of cartography and neural networks over the past decade (2013–2023), thus revealing the prevailing research trends and cutting-edge investigations in the field of machine learning and its application in mapping. In addition, this analysis included the systematic categorization of knowledge clusters arising from the fusion of cartography and neural networks, which was followed by the discernment of pivotal clusters in the field of knowledge mapping. Crucially, this study diligently identified the critical studies (milestones) that have made significant contributions to the development of these elucidated clusters. Timeline analysis was used to track these studies’ origins, evolution, and current status. Finally, we constructed collaborative networks among the contributing authors, journals, institutions, and countries. This mapping aids in identifying and visualizing the primary contributing factors of the evolution of knowledge mapping encompassing cartography and neural networks, thus facilitating interdisciplinary and multidisciplinary research and investigations.

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Section: Cluster #1 and #3-deep Learning And Machine Learningmentioning

confidence: 99%

Cartography and Neural Networks: A Scientometric Analysis Based on CiteSpace

Cheng,

Zhang,

Wang

et al. 2024

IJGI

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“…In the field of cartographic generalization, most existing research studies pertaining to settlement polygons focus on structured settlements, such as regular-shaped buildings [7], buildings with typical shapes [8], or arrangements [9][10][11][12], but limited studies address map features with certain semantics, e.g., urban villages. On the other hand, our preliminary research has identified that urban villages possess a distinctive morphology in 1:5K Digital Line Graphics (DLGs).…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Urban Village Extraction and Generalization from Digital Line Graphics Using the Computational Geometric Method and the Modified Hausdorff Distance

Gao,

Yan,

et al. 2024

IJGI

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Urban villages represent informal residential areas emerging since China’s rapid urbanization process. Scientific map generalization of urban villages with scientific maps aids readers in discerning their distribution and making informed decisions concerning them. However, there is still a scarcity of research on the automatic extraction and generalization of urban villages from vector data, which needs to be studied to further improve the expression of maps. To address this problem, this paper presents a methodology for the extraction and generalization of urban villages from Digital Line Graphics. Firstly, a heuristic approach is employed to analyze the atypical morphological characteristics of urban villages. Then, indices based on computational geometry and the modified Hausdorff distance are utilized to quantify these traits. Lastly, an automatic generalization principle for urban villages is offered. The approach was tested in experimental blocks and proved to be effective. It offers a novel method for the automatic extraction and cartography of urban villages.

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