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In traditional change detection methods, remote sensing images are the primary raster data for change detection, and the changes produced from cartography generalization in multi-scale maps are not considered. The aim of this research was to use a new kind of raster data, named map tile data, to detect the change information of a multi-scale water system. From the perspective of spatial cognition, a hierarchical system is proposed to detect water area changes in multi-scale tile maps. The detection level of multi-scale water changes is divided into three layers: the body layer, the piece layer, and the slice layer. We also classify the water area changes and establish a set of indicators and rules used for the change detection of water areas in multi-scale raster maps. In addition, we determine the key technology in the process of water extraction from tile maps. For evaluation purposes, the proposed method is applied in several test areas using a map of Tiandi. After evaluating the accuracy of change detection, our experimental results confirm the efficiency and high accuracy of the proposed methodology.

A Raster-Based Methodology to Detect Cross-Scale Changes in Water Body Representations Caused by Map Generalization