A Multilevel Road Alignment Model for Spatial-Query-by-Sketch

A sketch map represents an individual’s perception of a specific location. However, the information in sketch maps is often distorted and incomplete. Nevertheless, the main roads of a given location often exhibit considerable similarities between the sketch maps and metric maps. In this work, a shape-based approach was outlined to align roads in the sketch maps and metric maps. Specifically, the shapes of main roads were compared and analyzed quantitatively and qualitatively in three levels pertaining to an individual road, composite road, and road scene. An experiment was performed in which for eight out of nine maps sketched by our participants, accurate road maps could be obtained automatically taking as input the sketch and the metric map. The experimental results indicate that accurate matches can be obtained when the proposed road alignment approach Shape-based Spatial-Query-by-Sketch (SSQbS) is applied to incomplete or distorted roads present in sketch maps and even to roads with an inconsistent spatial relationship with the roads in the metric maps. Moreover, highly similar matches can be obtained for sketches involving fewer roads

A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships

<p>Linear feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified linear feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple linear feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These experiments suggest that Model 2, which considers only distance, does not provide stable results, while Models 3 and 4, which consider direction and topological relationships, produce stable results with levels of accuracy around 90% and 95%, respectively. The results suggest that the proposed method is simple yet robust for linear feature matching.</p