Network-Based Hierarchical Feature Augmentation for Predicting Road Classes in OpenStreetMap

Abstract

The need to enrich the semantic completeness of OpenStreetMap (OSM) data is crucial for its effective use in geographic information systems and urban studies. Addressing this challenge, our research introduces a novel hierarchical feature augmentation approach to developing machine learning classifiers by the features retrieved from various levels of road network connectivity. This method systematically augments the feature space by incorporating measure values of connected road features, thereby integrating extensive contextual information from the network hierarchy. In our evaluation, conducted across diverse urban landscapes in six cities in Italy and T & uuml;rkiye, we tested two geometry-, six centrality-, and eight semantic-based features to predict road functional classes stored as a highway = * key in OSM. The findings indicate a marginal impact of geometric features and city identifiers on classification performance. Utilizing centrality attributes alongside semantic features in a direct, non-hierarchical manner results in an F1 score of 80%. However, integrating these features in our network-based hierarchical feature augmentation process remarkably increases the F1 score up to 85%. The success of our approach underlines the importance of network-based feature engineering in capturing the complex dependencies of geographic data, considering a more accurate and contextually aware OSM classification framework.