Review of road selection methods for the purpose of multiscale mapping
Albert Adolf
Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of WarsawWarsaw, Poland
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and Izabela Karsznia
Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of WarsawWarsaw, Poland
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Aug 16, 2025
About this article
Published Online: Aug 16, 2025
Received: Apr 03, 2025
Accepted: May 23, 2025
DOI: https://doi.org/10.2478/mgrsd-2025-0031
Keywords
© 2025 Albert Adolf et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.
Figure 1.
Figure 2.
Information about most important characteristics of the research undertaken concerning road network selection_Selected references considered in this paper are included, as many of the works lack quantitative results’ evaluation
| ML | 1:100 000 | 1:200 000 | Accuracy | database generalization | |||
| MLSU-TAGCN | 81.40% | ||||||
| MLSU-GCN | 74.80% | ||||||
| MLSU-GAT | 75.51% | ||||||
| MLSU-GraphSAGE | 80.80% | ||||||
| Selection-4Fs | 71.20% | ||||||
| Selection-22Fs | 78.40% | ||||||
| ML | 1:10 000 | F1-score | database generalization | ||||
| 1:50 000 | GCN with functional semantic features | 89.74% | |||||
| 1:200 000 | 82.70% | ||||||
| ML | 1:250 000 | 1:1 000 000 | Accuracy | database generalization | |||
| HAN | 75.35% | ||||||
| ML | 1:250 000 | 1:500 000 | Accuracy | general geographic map | |||
| DT | 81.25% | ||||||
| RF | 84.38% | ||||||
| SVM | 84.38% | ||||||
| DTGA | 90.00% | ||||||
| NN | 81.88% | ||||||
| ML | 1:10 000 | 1:50 000 | F1 Score | database generalization | |||
| GNN | 92.10% | ||||||
| AHP | 88.00% | ||||||
| Graph | 1:50 000 | 1:100 000 | Accuracy | database generalization | |||
| Road-path selection constrained by settlements | 86.00% | ||||||
| Graph | 1:10 000 | „Large-scale” | Selected-Source Correlation | urban road generalization | |||
| Functional node elimination | Pearson (ρ) = 0.964 | ||||||
| Spearman (R) = 0.911 | |||||||
| ML | 1:250 000 | Accuracy | database generalization | ||||
| 1:500 000 | DT | 84.46% | |||||
| DTGA | 83.33% | ||||||
| RF | 84.96% | ||||||
| 1:1 000 000 | DT | 99.18% | |||||
| DTGA | 99.44% | ||||||
| RF | 99.34% | ||||||
| Mesh | 1:10 000 | 1:50 000 | Shape similarity overlap | topographic map | |||
| Direct pair merging | 100% | ||||||
| Iterative area elimination | 100% | ||||||
| ML | 1:10 000 | 1:100 000 | Accuracy | database generalization | |||
| MLP | 85.83% | ||||||
| JK-GAT | 88.12% | ||||||
| Res-GAT | 87.88% | ||||||
| Dense-GAT | 87.41% | ||||||
| Stroke | 1:5 000 | 1:200 000 | Common stroke ratio | database generalization | |||
| AHP | 89% | ||||||
| Yu et al. | 2020 | Stroke | Unknown | Maximum similarity | navigation | ||
| 1:5 000 | Traffic Flow Radical Law Strokes | 61.15% | |||||
| 1:25 000 | 65.86% | ||||||
| 1:50 000 | 90.95% | ||||||
| 1:5 000 | Traffic Flow Pair Strokes | 61.61% | |||||
| 1:25 000 | 65.58% | ||||||
| 1:50 000 | 90.95% | ||||||
| Mesh, Stroke | 1:10 000 | 1:50 000 | Maximum similarity | topographic map | |||
| Mesh elimination | 89.52% | ||||||
| Stroke-edge elimination | 91.64% | ||||||
| Park, Huh | 2019 | ML | 1:5 000 | 1:25 000 | Matching ratio | topographic map | |
| Logistic Regression | 81.66% | ||||||
| Stroke | 1:10 000 | 1:50 000 | Accuracy | database generalization | |||
| Stroke generation with weighted Voronoi diagrams | 88.80% | ||||||
| ML | Accuracy | database generalization | |||||
| 1:20 000 | 1:50 000 | MP | 80.45% | ||||
| SVM | 77.05% | ||||||
| BLR | 80.90% | ||||||
| 1:100 000 | MP | 79.90% | |||||
| SVM | 81.10% | ||||||
| BLR | 80.65% | ||||||
| 1:200 000 | MP | 91.55% | |||||
| SVM | 92.90% | ||||||
| BLR | 92.35% | ||||||
| 1:50 000 | 1:250 000 | MP | 83.20% | ||||
| SVM | 82.70% | ||||||
| BLR | 83.10% | ||||||
| Stroke | 1:10 000 | 1:200 000 | Mean improvement (vs. Basic) | database generalization | |||
| Enhanced stroke generation | 67.88% | ||||||
| Stroke, Mesh | 1:10 000 | 1:50 000 | Satisfaction of hard constraints | database generalization | |||
| Extended stroke–mesh combination | 100% | ||||||
| ML | 1:20 000 | Accuracy | map updates | ||||
| 1:50 000 | BPNN | 82.4% | |||||
| 1:100 000 | 87% | ||||||
| 1: 200 000 | 98.6% | ||||||
| Stroke, Mesh | Accuracy | database generalization | |||||
| 1:20 000 | 1:50 000 | Stroke generation | 84.7% | ||||
| 1:100 000 | 76.7% | ||||||
| 1:200 000 | 68.5% | ||||||
| 1:50 000 | 1:250 000 | 77.3% | |||||
| 1:20 000 | 1:50 000 | Mesh density | 67.7% | ||||
| 1:100 000 | 63.7% | ||||||
| 1:200 000 | 61.6% | ||||||
| 1:50 000 | 1:250 000 | 71.4% | |||||
| Graph | No. of road segments | navigation | |||||
| Scale free | Top 2% strokes | Ego network | 82.1% | ||||
| Top 10% strokes | 89.9% | ||||||
| Top 15% strokes | 92.5% | ||||||
| Top 20% strokes | 92.6% | ||||||
| Top 2% strokes | Weighted ego network | 87.1% | |||||
| Top 10% strokes | 95.8% | ||||||
| Top 15% strokes | 94% | ||||||
| Top 20% strokes | 94.6% | ||||||
| Mesh | Selected road length change | database generalization | |||||
| 1:25 000 | 1:50 000 | Urban block amalgamation | 14.10% | ||||
| 1:100 000 | −17.30% | ||||||
| Stroke | 1:1 000 | Not specified | Mean similarity with target | database generalization | |||
| Hierarchical stroke generation | 40.75% | ||||||
| Stroke, Mesh | 1:50 000 | 1:100 000 | Road length overlap | database generalization | |||
| Enriched structural selection | 97% | ||||||
| Stroke | 1:10 000 | 1:50 000 | Avg no of identical strokes | database generalization | |||
| Stroke generation with seed extension | 91.84% | ||||||
| Mesh | 1:10 000 | 1:50 000 | Mean consistency with existing map | map updates | |||
| Mesh density-based selection | 89.50% |