Make It Simple
Effective Road Selection for Small-Scale Map Design Using Decision-Tree-Based Models
Bibliographic Data
| ID | 22033674 |
|---|---|
| Authors | Izabela Karsznia (0000-0001-5510-8770, University of Warsaw, corresponding author), Karolina Sielicka (0000-0003-2330-2426, University of Warsaw), Karolina Wereszczyńska (University of Warsaw), Robert Weibel (0000-0002-2425-0077, University of Zurich) |
| Year | 2022 |
| Volume | 11 |
| Issue | 8 |
| Pages | 457 |
| Publication date | 2022-08-22 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi11080457 |
| OpenAlex | W4292687257 |
| Language | EN |
| Citations received | 4 |
| References cited | 18 |
The complexity of a road network must be reduced after a scale change, so that the legibility of the map can be maintained. However, deciding whether to show a particular road section on the map is a very complex process. This process, called selection, constitutes the first step in a sequence of further generalization operations and it is a prerequisite to effective road network generalization. So far, not many comprehensive solutions have been developed for effective road selection specifically at small scales as the studies have mainly dealt with large-scale maps. The paper presents an experiment using machine learning (ML), specifically decision-tree-based (DT) models, to optimize the selection of the roads from 1:250,000 to 1:500,000 and 1:1,000,000 scales. The scope of this research covers designing and verifying road selection models on the example of three selected districts in Poland. The aim is to consider the problem of road generalization holistically, including numerous semantic, geometric, topological, and statistical road characteristics. The research resulted in a list of measurable road attributes that comprehensively describe the rank of a particular road section. The outcome also includes attribute weights, attribute correlation calculated for roads, and machine learning models designed for automatic road network selection. The performance of the machine learning models is very high and ranges from 80.94% to 91.23% for the 1:500,000 target scale and 98.21% to 99.86% for the 1:1,000,000 scale
Cartographic generalization · Cartography · Data mining · Decision tree · Generalization · Geography · Legibility · Machine learning · Road map · Automated Road and Building Extraction · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Mathematics · Artificial Intelligence
A Selection Method of Massive Point Cluster Using the Delaunay Triangulation to Support Real-Time Visualization
Road Network Intelligent Selection Method Based on Heterogeneous Graph Attention Neural Network
Using machine learning and data enrichment in the selection of roads for small-scale maps
Artificial intelligence studies in cartography
Application of AHP to Road Selection
Deep Graph Convolutional Networks for Accurate Automatic Road Network Selection
When Traditional Selection Fails
Integrating Thematic, Geometric, and Topological Information in the Generalization of Road Networks
Improving settlement selection for small-scale maps using data enrichment and machine learning
Road network selection for medium scales using an extended stroke-mesh combination algorithm
| Unique citing works | 4 |
|---|---|
| Citations per year | 1.33 |
| Citation span | 2023 - 2025 (3) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 4 |