Automatic Change Detection Based on Areal Feature Matching in Different Network Data-sets

Abstract

By a development of car navigation systems and mobile or positioning technology, it increases interest in location based services, especially pedestrian navigation systems. Updating of digital maps is important because digital maps are mass data and required to short updating cycle. In this paper, we proposed change detection for different network data-sets based on areal feature matching. Prior to change detection, we defined type of updating between different network data-sets. Next, we transformed road lines into areal features(block) that are surrounded by them and calculated a shape similarity between blocks in different data-sets. Blocks that a shape similarity is more than 0.6 are selected candidate block pairs. Secondly, we detected changed-block pairs by bipartite graph clustering or properties of a concave polygon according to types of updating, and calculated Fréchet distance between segments within the block or forming it. At this time, road segments of KAIS map that Fréchet distance is more than 50 are extracted as updating road features. As a result of accuracy evaluation, a value of detection rate appears high at 0.965. We could thus identify that a proposed method is able to apply to change detection between different network data-sets.

A Polygon and Point-Based Approach to Matching Geospatial Features

“…However, the majority of the current approaches concentrate on network matching and mainly on road networks [18][19][20][21][22][23][24][25][26][27]. In contrast to network matching, there are few studies concerning matching area; nevertheless, despite this shortage of research, there are relevant area-based matching algorithms that must be analyzed.…”