Level-Aware Consistent Multilevel Map Translation From Satellite Imagery

With the rapid development of remote sensing technology, the quality of satellite imagery (SI) is getting higher, which contains rich cartographic information that can be translated into maps. However, existing methods either only focus on generating single-level map or do not fully consider the challenges of multilevel translation from satellite imageries, i.e., the large domain gap, level-dependent content differences, and main content consistency. In this article, we propose a novel level-aware fusion network for the SI-based multilevel map generation (MLMG) task. It aims to tackle these three challenges. To deal with the large domain gap, we propose to generate maps in a coarse-to-fine way. To well-handle the level-dependent content differences, we design a level classifier to explore different levels of the map. Besides, we use a map element extractor to extract the major geographic element features from satellite imageries, which is helpful to keep the main content consistency. Next, we design a multilevel fusion generator to generate a consistent multilevel map from the multilevel preliminary map, which further ensures the main content consistency. In addition, we collect a high-quality multilevel dataset for SI-based MLMG. Experimental results show that the proposed method can provide substantial improvements over the state-of-the-art alternatives in terms of both objective metric and visual quality.