A Stacking Ensemble Learning Method to Classify the Patterns of Complex Road Junctions

Abstract

Recognizing the patterns of road junctions in a road network plays a crucial role in various applications. Owing to the diversity and complexity of morphologies of road junctions, traditional methods that rely heavily on manual settings of features and rules are often problematic. In recent years, several studies have employed convolutional neural networks (CNNs) to classify complex junctions. These methods usually convert vector-based junctions into raster representations with a predefined sampling area coverage. However, a fixed sampling area coverage cannot ensure the integrity and clarity of each junction, which inevitably leads to misclassification. To overcome this drawback, this study proposes a stacking ensemble learning method for classifying the patterns of complex road junctions. In this method, each junction is first converted into raster images with multiple area coverages. Subsequently, several CNN-based base-classifiers are trained using raster images, and they output the probabilities of the junction belonging to different patterns. Finally, a meta-classifier based on random forest is used to combine the outputs of the base-classifiers and learn to arrive at the final classification. Experimental results show that the proposed method can improve the classification accuracy for complex road junctions compared to existing CNN-based classifiers that are trained using raster representations of junctions with a fixed sampling area coverage.

Collaborative Methods of Resolving Road Graphic Conflicts Based on Cartographic Rules and Generalization Operations

“…Road generalization is an important research direction in the field of map generalization [1][2][3]. It mainly includes road selection [4,5], road simplification [6,7], road pattern recognition [8], and road displacement [9][10][11].…”

CatBoost-Based Automatic Classification Study of River Network

“…It can combine and construct the basic model according to different ideas and combine multiple learners to complete the task, so as to achieve better purposes. It has been extensively studied and applied in the fields of classification and prediction [37,38], object detection [39,40], and text classification [41,42]. However, there is currently a lack of research on the automatic classification of river networks using the ensemble learning method.…”