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A POI selection method based on GCN considering annotation conflicts during map scale transformation
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AI
DOI：10.1080/13658816.2025.2466113.png)
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Abstract
En 中文
Points of interest (POIs) are critical components of maps, often accompanied by annotations that convey essential information. However, when maps are scaled down, annotation sizes generally remain unchanged, frequently resulting in overlaps and conflicts that compromise map clarity. To address this issue, this article incorporates annotation conflicts as constraints within the POI selection decision-making process during scale transformations. It presents a novel POI selection method using a graph convolutional network (GCN) considering annotation conflicts. The proposed method begins by constructing a graph structure based on Delaunay triangulation, which represents second-order proximity relationships. Node features are then extracted from three dimensions: semantic, spatial and annotation, while annotation conflicts are abstracted as edge weights within the graph. Leveraging the TAGCN network, a POI selection model is developed, enabling intelligent POI selection through semi-supervised training. This approach transforms the POI selection task into a classification problem, seamlessly integrating expert knowledge into the deep learning framework through samples and models. Moreover, this article introduces an annotation placement algorithm to further mitigate annotation conflicts. Experimental results demonstrate that this method effectively reduces annotation conflicts while preserving map details, outperforming Maplex in ArcGIS.
Keywords:
POI selection
annotation conflict
GCN
annotation placement
scale transformation
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