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
| Author | Year | Approach | Source scale | Target scale | Method | Major metric | Major purpose |
|---|---|---|---|---|---|---|---|
| Xiao et al. | 2024 | 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% | ||||||
| Tang et al. | 2024 | ML | 1:10 000 | F1-score | database generalization | ||
| 1:50 000 | GCN with functional semantic features | 89.74% | |||||
| 1:200 000 | 82.70% | ||||||
| Zheng et al. | 2024 | ML | 1:250 000 | 1:1 000 000 | Accuracy | database generalization | |
| HAN | 75.35% | ||||||
| Karsznia et al. | 2024a | 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% | ||||||
| Guo et al. | 2023 | ML | 1:10 000 | 1:50 000 | F1 Score | database generalization | |
| GNN | 92.10% | ||||||
| AHP | 88.00% | ||||||
| Lyu et al. | 2022 | Graph | 1:50 000 | 1:100 000 | Accuracy | database generalization | |
| Road-path selection constrained by settlements | 86.00% | ||||||
| Pung et al. | 2022 | Graph | 1:10 000 | „Large-scale” | Selected-Source Correlation | urban road generalization | |
| Functional node elimination | Pearson (ρ) = 0.964 | ||||||
| Spearman (R) = 0.911 | |||||||
| Karsznia et al. | 2022 | 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% | ||||||
| Wu et al. | 2022 | Mesh | 1:10 000 | 1:50 000 | Shape similarity overlap | topographic map | |
| Direct pair merging | 100% | ||||||
| Iterative area elimination | 100% | ||||||
| Zheng et al. | 2021 | 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% | ||||||
| Han et al. | 2020 | 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% | ||||||
| Li et al. | 2020 | 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% | ||||||
| Zhang et al. | 2017 | Stroke | 1:10 000 | 1:50 000 | Accuracy | database generalization | |
| Stroke generation with weighted Voronoi diagrams | 88.80% | ||||||
| Zhou, Li | 2017 | 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% | ||||||
| Weiss, Weibel | 2014 | Stroke | 1:10 000 | 1:200 000 | Mean improvement (vs. Basic) | database generalization | |
| Enhanced stroke generation | 67.88% | ||||||
| Benz, Weibel | 2014 | Stroke, Mesh | 1:10 000 | 1:50 000 | Satisfaction of hard constraints | database generalization | |
| Extended stroke–mesh combination | 100% | ||||||
| Zhou, Li | 2014 | ML | 1:20 000 | Accuracy | map updates | ||
| 1:50 000 | BPNN | 82.4% | |||||
| 1:100 000 | 87% | ||||||
| 1: 200 000 | 98.6% | ||||||
| Li et al. | 2012 | 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% | |||||
| Zhang, Li | 2011 | 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% | ||||||
| Gülgen, Gökgöz | 2011 | Mesh | Selected road length change | database generalization | |||
| 1:25 000 | 1:50 000 | Urban block amalgamation | 14.10% | ||||
| 1:100 000 | −17.30% | ||||||
| Yang et al. | 2011 | Stroke | 1:1 000 | Not specified | Mean similarity with target | database generalization | |
| Hierarchical stroke generation | 40.75% | ||||||
| Touya | 2010 | Stroke, Mesh | 1:50 000 | 1:100 000 | Road length overlap | database generalization | |
| Enriched structural selection | 97% | ||||||
| Liu et al. | 2010 | Stroke | 1:10 000 | 1:50 000 | Avg no of identical strokes | database generalization | |
| Stroke generation with seed extension | 91.84% | ||||||
| Chen et al. | 2009 | Mesh | 1:10 000 | 1:50 000 | Mean consistency with existing map | map updates | |
| Mesh density-based selection | 89.50% |