Map Generalization for the Future: Editorial Comments on the Special Issue

Kronenfeld, Barry J.; Buttenfield, Barbara P.; Stanislawski, Lawrence V.

doi:10.3390/ijgi9080468

Cited by 11 publications

(2 citation statements)

References 8 publications

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“…Despite the evolution of automation in map production, there is a lack of a highimpact methodology concerning generalization modeling [22]. Emerging research on deep learning integration in generalization modeling has appeared, but it is still at an early stage [22][23][24].…”

Section: Background and Recent Achievements On The Evaluation Of Gene...mentioning

confidence: 99%

A Constraint-Based Generalization Model Incorporating a Quality Control Mechanism

Blana

Kavadas²,

Tsoulos

2023

Geographies

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Automation in map production has created the need for modeling the map composition process. Generalization is the most critical process in map composition, with considerable impact on the quality of features portrayed on the maps. Modeling of the generalization process has been an area of research for several years in the international cartographic community. Constraint-based generalization modeling prevailed, and it is evolving to an agent model or to other optimization models. The generalization model presented in this paper is based on constraint-based modeling. It introduces the standardization of the semantic and cartographic generalization process together with an evaluation mechanism for the assessment of the quality of the resulting cartographic data considering simultaneously the preservation of the shape of the portrayed linear and area features. For cartographers, quality management is a key factor in creating an evidence-based, reliable product. To achieve this objective, cartographers, drawing on international experience, should implement a quality policy and adopt a quality management system (QMS) as an integral part of the map production process, starting with the quality assessment of the input data and finishing with the evaluation of the final product.

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Section: Background and Recent Achievements On The Evaluation Of Gene...mentioning

confidence: 99%

A Constraint-Based Generalization Model Incorporating a Quality Control Mechanism

Blana

Kavadas²,

Tsoulos

2023

Geographies

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“…Roads, as essential skeletal elements on maps, not only form a crucial component of maps but also command the attention and interest of both map users and cartographers. Given their significant roles in both social and military contexts, roads have become a focal point of extensive research and practical exploration worldwide, particularly in the realm of automatic road network selection [3][4][5].…”

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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Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals

Sharma,

Arumugam

2024

Lecture Notes in Networks and Systems

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Map Generalization for the Future: Editorial Comments on the Special Issue

Abstract: Generalization of geospatial data is a cornerstone of cartography, a sequence of often unnoticed operations that lays the foundation of visual communication [...]

Cited by 11 publications

References 8 publications

A Constraint-Based Generalization Model Incorporating a Quality Control Mechanism

A Constraint-Based Generalization Model Incorporating a Quality Control Mechanism

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

Pattern Recognition: An Outline of Literature Review that Taps into Machine Learning to Achieve Sustainable Development Goals

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