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A Multi-task Learning Framework for Road Attribute Updating via Joint Analysis of Map Data and GPS Traces

The quality of a digital map is of utmost importance for geo-aware services. However, maintaining an accurate and up-to-date map is a highly challenging task that usually involves a substantial amount of manual work. To reduce the manual efforts, methods have been proposed to automatically derive road attributes by mining GPS traces. However, previous methods always modeled each road attribute separately based on intuitive hand-crafted features extracted from GPS traces. This observation motivates us to propose a machine learning based method to learn joint features not only from GPS traces but also from map data. To model the relations among the target road attributes, we extract low-level shared feature embeddings via multi-task learning, while still being able to generate task-specific fused representations by applying attention-based feature fusion. To model the relations between the target road attributes and other contextual information that is available from a digital map, we propose to leverage map tiles at road centers as visual features that capture the information of the surrounding geographic objects around the roads. We perform extensive experiments on the OpenStreetMap where state-of-the-art classification accuracy has been obtained compared to existing road attribute detection approaches.