Constructing online High-Definition (HD) maps is crucial for the static environment perception of autonomous driving systems (ADS). Existing solutions typically attempt to detect vectorized HD map elements with unified models; however, these methods often overlook the distinct characteristics of different non-cubic map elements, making accurate distinction challenging. To address these issues, we introduce an expert-based online HD map method, termed MapExpert. MapExpert utilizes sparse experts, distributed by our routers, to describe various non-cubic map elements accurately. Additionally, we propose an auxiliary balance loss function to distribute the load evenly across experts. Furthermore, we theoretically analyze the limitations of prevalent bird's-eye view (BEV) feature temporal fusion methods and introduce an efficient temporal fusion module called Learnable Weighted Moving Descentage. This module effectively integrates relevant historical information into the final BEV features. Combined with an enhanced slice head branch, the proposed MapExpert achieves state-of-the-art performance and maintains good efficiency on both nuScenes and Argoverse2 datasets.

Most existing learning-based single image super-resolution (SISR) methods mainly focus on improving reconstruction accuracy, but they always generate overly smoothed results that fail to match the visual perception. Although perceptual quality can be greatly improved via introducing adversarial loss, image fidelity may decrease to some extent. Moreover, most methods are trained and evaluated on simulated datasets and their performance would drop significantly on real remote sensing imagery. To solve the above problems, we propose a new SISR algorithm named gradient enhanced dual regression network (GEDRN). Based on the dual regression framework, we use share-source residual structure and non-local operation to learn abundant low-frequency information and long-distance spatial correlations. Besides, we not only introduce additional gradient information to avoid blurry results but also apply gradient loss and perceptual loss to further improve the perceptual quality. Our GEDRN is trained and tested on real-world multi-sensor satellite images. Experimental results demonstrate the superiority of the proposed method in achieving much better perceptual quality and ensuring high fidelity.