Translation of Aerial Image Into Digital Map via Discriminative Segmentation and Creative Generation

Automatic translation of aerial images into digital maps is an important and challenging task which is widely used in practical applications. Most of the existing works view it either as a creative image-to-image translation problem or a discriminative semantic segmentation problem. However, we notice that human annotators need to extract and understand the information in aerial images first and then translate them to online maps in a creative way, which helps them draw accurate and visually appealing online maps. In this article, we propose an end-to-end online map generation method that combines a discriminative module with a creative module based on this observation to mimic human behavior. Specifically, we first utilize a semantic segmentation module to obtain a rough aerial map, in which each region is labeled with its category, and then further improve its quality with a creative module. To train a robust network that generalizes well to unfamiliar regions, we also collect a large aerial image dataset for online map generation (AIDOMG). AIDOMG consists of 40 087 pairs of aerial images and corresponding online maps collected from nine regions of six continents. We conduct extensive experiments to verify the superiority of the new design that combines discrimination and creativity and experimental results show that the performance of the proposed method significantly outperforms baseline methods.