Maps are widely used to visualize geo-information so that map users can develop related understandings about the real world. Such a process for communicating information is largely dependent on the rendering of map elements using different symbols (points and linear and area symbols). To meet the demand of more dynamic and comprehensive visualization in map rendering, it is essential to improve the rendering efficiency. This paper focuses on these research topics, especially the difficulty in constructing and drawing linear map symbols. By employing shader language, a function-based linear symbol building and rendering method is presented in this paper. The basic idea of this function-based method is to build a map-rendering solution that employs graphic processing unit (GPU) acceleration technology to improve the rendering efficiency. A ‘function’ is used to represent the algorithm that draws certain simple or complex linear map symbols. This function reflects the structure of a linear map symbol (describing the symbol construction information) and also the rendering process of the symbolized linear map elements (handled on a per-pixel basis by the shader program). Based on the Open Geospatial Consortium (OGC), Styled Layer Descriptor (SLD) specifications, four basic line types (i.e., solid lines, dashed lines, gradient color lines, and transition lines) are implemented in the proposed method, and the implementation of line markers, line joins and line caps is also discussed. Three experiments are conducted to demonstrate improvements in map rendering. The results show that a variety of linear map symbols can be constructed in a uniform way, which suggests that the proposed method addresses the difficulty in drawing linear map symbols. With this method, the efficiency of rendering linear map elements is substantially improved compared to using the graphics device interface plus (GDI+) and anti-grain geometry (AGG) methods; it also provides an applicable approach for developing map rendering systems. Using this function-based concept, the complexity of building linear map symbols and drawing linear map elements can be decreased.

The timely and accurate mapping of dynamic disasters and emergencies is an important task that is necessary for supporting the decision-making that can improve the efficiency of rescue and response efforts. The existing emergency symbol libraries are primarily composed of point symbols and simple line symbols, focusing on the representation of disasters, related facilities, and operations. However, various existing response factors (e.g., the distribution and types of emergency forces) are also important for further decision-making and emergency responses; there is a need to design complex and diverse symbols to represent this rich information. Moreover, traditional mapping systems only provide static map symbols that cannot be easily edited after creation, making it difficult to support interactive editing after the symbols are mapped, thus hindering the representation of dynamic disasters and response factors. This article targets a solution of the above issues by proposing a procedural construction method of interactive map symbols for dynamic disasters and emergency responses. There are two primary research points. First, an emergency response and decision symbol library was classified and integrated into the existing attachments to form a richer symbol library for comprehensively representing disasters and emergencies. Second, an interactive map symbol procedural construction method was designed based on (1) primitive geometric compositions and geometric graphics algorithms to construct the map symbol graphics; (2) an interactive graphics control and drawing attributes configuration method to support user interactive editing of the visual variables of the mapped symbols; (3) and a dynamic updating and drawing strategy to support the real-time refreshing of the changing visual variables. The experiment was conducted using the Wenchuan earthquake as a case study, and the results demonstrate a powerful capacity of the produced interactive map symbols, which will contribute to the improvement of the mapping efficiency and representation capability of disasters and emergency response.

Accurate and timely access to building rooftop information is very important for urban management. The era of big data brings new opportunities for rooftop extraction based on deep learning and high-resolution satellite imagery. However, collecting representative datasets from such big data to train deep learning models efficiently is an essential problem that still needs to be explored. In this study, geospatial stratified and optimized sampling (GSOS) based on geographical priori information and optimization of sample spatial location distribution is proposed to acquire representative samples. Specifically, the study area is stratified based on land cover to divide the rooftop-dense stratum and the rooftop-sparse stratum. Within each stratum, an equal amount of samples is collected and their spatial locations are optimized. To evaluate the effectiveness of the proposed strategy, several qualitive and quantitative experiments are conducted. As a result, compared with other common sampling approaches (e.g., random sampling, stratified random sampling, and optimized sampling), GSOS is superior in terms of the abundance and types of collected samples. Furthermore, two quantitative metrics, the F₁-score and Intersection over Union (IoU), are reported for rooftop extraction based on deep learning methods and different sampling methods, in which the results based on GSOS are on average 9.88% and 13.20% higher than those based on the other sampling methods, respectively. Moreover, the proposed sampling strategy is able to obtain representative training samples for the task of building rooftop extractions and may serve as a viable method to alleviate the labour-intensive problem in the construction of rooftop benchmark datasets.