Automatic extraction of roads from remote sensing imagery has numerous applications, such as urban planning and navigation. However, the quality of images is limited, and most existing road extraction methods suffer from discontinuity. Additionally, there remains a gap between pixel-based road segmentation and road vectorization. To address these challenges, we introduce a method that utilizes a fusion attention network (MFFANet) and regularization algorithm to extract road surfaces, centerlines, edges, and intersections. MFFANet comprises three components. The complementary feature embedding module (CFEM) adaptively encodes remote sensing images, vehicle trajectories and OSM points to capture specific modal features. The multistage features fusion module (MFFM) is proposed to improve the completeness and connectivity of road extraction by integrating multisource geospatial features. The multilevel mask generation module (MMGM) enhances road segmentation results through a weighting mechanism that can simultaneously predict local road segments, road sections, and road network masks. Additionally, a novel joint loss function is introduced to balance local and global optimization. In the regularization stage, fused hierarchical masks generate road segments, with a skeleton refinement for centerlines and widths, followed by smooth segment reconstruction and extraction of edges and intersections. Experiments on different datasets demonstrate that our designed fusion attention network outperforms the latest road segmentation models; our regularization algorithm shows strong robustness and the comprehensive metrics of vectorized road line extraction exceeds 70%.

Modern technology has given thousands of amateur cartographers not only the opportunity but also the means to make a valuable contribution to mapping at all scales. However, web maps made by amateurs are prone to legibility shortcomings, such as coalescence, complexity, and congestion. These problems can be solved by map generalization; however, for amateurs participating in web mapping, the crucial decision is when to activate generalization. Such a decision is difficult, as the original and target map scales are sometimes uncertain owing to heterogeneous data quality and different output devices. In this study, starting from the identification of different pixels in a rasterized line, we developed a new scale-specific measure to evaluate the degree of legibility (DoL) of individual lines. Our experimental results showed that this measure could reflect the legibility of a line at a given map scale effectively, and could facilitate accurate decisions on whether generalization is necessary. In addition, we fitted a multi-scale legibility function (MSLF) for each line based on DoLs at multiple scales. This function can be used to estimate the legible scale for each line accurately. Our measures are easy to understand, tolerant of data quality, and have great potential for multi-scale spatial data processing.

Taking location-based service (LBS) as the research scenario and aiming at the limitation of visualizing LBS points of interest (POI) in conventional web maps, this article proposes a visualization method of LBS-POI based on tag cloud, which is called “LBS tag cloud”. In this method, the user location is taken as the layout center, and the name of the POI is converted into a text tag and then placed around the center. The tags’ size, color, and placement location are calculated based on other attributes of the POI. The calculation of placement location is at the core of the LBS tag cloud. Firstly, the tag’s initial placement position and layout priority are calculated based on polar coordinates, and the tags are placed in the initial placement position in the order of layout priority. Then, based on the force-directed model, a repulsive force is applied to the tag from the layout center to make it move to a position without overlapping with other tags. During the move, the quadtree partition of the text glyph is used to optimize the detection of overlaps between tags. Taking scenic spots as an example, the experimental results show that the LBS tag cloud can present the attributes and distribution of POIs completely and intuitively and can effectively represent the relationship between the POIs and user location, which is a new visualization form suitable for spatial cognition.