关键词: POI标签, 深度学习, 神经网络, 多层次模型

Abstract: Point of interest (POI) is one of the main elements of electronic maps, navigation and other applications. Its data quality directly affects the level of intelligence of geographic information services. In view of the non-professional collection characteristics of data on public geographic information platforms such as OpenStreetMap (OSM), the POI data labels often have quality problems such as missing labels or incorrect labels. Thus, there is an urgent need for intelligent inference of POI labels. The conventional neural network model predicts multi-category data labels directly from a single level, which does not consider the problem of the uneven distribution of POI categories. The labels predicted by neural network tend to data categories which contain larger data volume, where the learning algorithm is difficult to generalize small-scale sample rules. This paper takes into account the massive gaps in the data scale between different POI categories, proposing a neural network prediction method based on multi-level POI category organization. Through the hierarchical aggregation of small sample categories, the structured POI category tree is established, achieving a relatively balanced category division of the data scales at different levels of the tree, which supports the high-precision prediction of labels. Experiments show that based only on the POI location information, the accuracy of this method is higher than those of the traditional methods.

Key words: POI label, deep learning, neural network, multi-level model