Cartography and Geographic Information Science

Zhang, Yifan; He, Zhengting; Li, Jingxuan; Lin, Jianfeng; Guan, Qingfeng; Yu, Wenhao

doi: 10.1080/15230406.2024.2404868pmid: N/A

The mapping process generally involves intricate operations, such as symbol design, layout design, and text annotation, demanding a high level of professional expertise. The high requirement for map producers hinders the promotion and widespread adoption of mapping. Consequently, researchers are concentrating on techniques to automate and enhance the intelligence of the mapping process. For example, some studies attempt to train deep learning models for mapping, including methods like map style transfer. However, these approaches typically treat the entire map as a global input and generate a new map as output, lacking the flexibility to consider and control detailed elements within a map. Therefore, in this paper, we propose a large language model-based intelligent mapping framework, termed MapGPT, which can be used for mapping by considering the map as an integration of various map elements. Specifically, multiple professional mapping tools are designed in MapGPT, and each tool is designed to control a corresponding map element. With these tools, a large language model is used to first understand the demand of users based on mere natural language descriptions, and subsequently automatically invoke appropriate tools in sequence to generate a map. Furthermore, by utilizing a memory component to store interaction information, users can interact with MapGPT through conversation to adjust map elements such as color and position. In conclusion, MapGPT offers user-friendly mapping experience, showing potential to be a mapping assistant for professional map producers. A comprehensive demonstration of this framework is provided in a visual case study video, accessible at https://github.com/AGI-GIS/MapGPT.

Ma, Mengkai; Dong, Jian; Tang, Lulu; Wang, Zimeng

doi: 10.1080/15230406.2024.2305473pmid: N/A

The automatic extraction of bottom sediment annotations in large-scale raster nautical charts has limitations, including an imprecise semantic information description and low efficiency. To overcome them, we propose a convolutional neural network (CNN)-based method for the automatic extraction of bottom sediment annotations in raster nautical charts, using image processing techniques to improve it. First, an adaptive chart partitioning model that considers element completeness is constructed. Second, a principle for the unique identification of elements based on spatial conflicts is designed. Finally, a model for accurately extracting semantic information for bottom sediment annotations is established. To evaluate the effectiveness of the proposed method, we implemented a model based on the PyTorch framework and used the PIL library to analyze the results. We also conducted comparative experiments on multiple CNN models to recommend the selection of such models in the proposed method by comparing their classification and recognition performance. The experimental results indicate that (1) the proposed model can achieve high-precision extraction of bottom sediment annotations in raster nautical charts. (2) Furthermore, the proposed model generally has high recognition accuracy and semantic completeness, with better recognition precision than traditional pattern recognition methods.

Wei, Zhiwei; Ding, Su; Xu, Wenjia; Fang, Jifei; Liu, Chunbo; Wang, Yang

doi: 10.1080/15230406.2024.2311259pmid: N/A

A flow map is a type of thematic visualization that depicts the movement of objects across a geographical space using a tree layout resembling a natural river system. In this paper, we introduce an innovative and automated approach called REA-FM, which leverages the power of the maze-solving algorithm to extract rivers from digital elevation models (DEMs). This enables the creation of flow maps that originate from a single source and extend to multiple destinations. Initially, REA-FM represents the mapping space of a flow map using a DEM. Subsequently, a maze-solving algorithm is adapted to extract flow paths from the destinations to the origin within the DEM data, with constraints on search directions, direction weights, and search ranges based on quality criteria specific to flow maps. To obtain comprehensive flow maps, the maze-solving algorithm is employed iteratively, considering the importance of each flow path, as determined by their respective lengths. These obtained paths are finally rendered smoothly with varying widths using Bézier curves, thereby enhancing the visual aesthetics of the flow map. A comparative evaluation with existing approaches demonstrates that REA-FM can generate natural-looking flow maps with reduced total length and improved node distribution, eliminating node overlaps and edge crossings. Furthermore, the effectiveness of REA-FM is validated through three extension experiments involving heterogeneous mapping spaces and areas with obstacles. Parameter analysis confirms that REA-FM offers intuitive control over the layout of flow maps. Project website: https://github.com/TrentonWei/FlowMap

Wen, Yi; Zhou, Xiran; Li, Kaiyuan; Li, Honghao; Yan, Zhigang

doi: 10.1080/15230406.2024.2368574pmid: N/A

The information contained in a map is always represented by text, symbols, and map-type. Among them, map-type is a critical element that denotes the category and theme of map content, which can support map content extraction, map retrieval, and other map data mining tasks. However, the representations of map-type are always so complex and diverse that relies on multiple descriptive labels. Traditional deep learning methods regarding map-type classification are developed by single label, which only supports single-task classification. This means these approaches might overlook the common features among multiple map-type. In this paper, we propose a framework of multi-task deep learning strategy for employing the state-of-the-art deep convolutional neural network models, including ResNet50, MobileNetV2, and Inception-v3, to conduct efficient multi-label map-type classification. Specifically, we develop the dedicated classification module and label selection layer, and integrate them into the backbone of the deep convolutional network model. The experiments revealed that our proposed multi-task classification strategy can achieve greater accuracy in map-type classification, with less processing time required compared to state-of-the-art deep learning regarding map-type classification. This proves that multi-task classification strategy could be competitive to recognize and discover the complex map-type information.

Wang, Zhuo; Yan, Haowen; Wang, Xiaolong; Wang, Bingxuan; Ying, Shen

doi: 10.1080/15230406.2024.2392795pmid: N/A

As a specialized map product, Wemaps must comply with relevant laws and regulations. Map audit plays a crucial role in ensuring map quality by preventing the production and dissemination of problem maps, as well as safeguarding national sovereignty, security, and interests. The user base for Wemaps is diverse, encompassing various types of maps, vast amounts of map data, and high expectations for timely dissemination. However, the current map audit process is inefficient and burdensome, failing to meet the specific needs of Wemaps audits. The key to solving this problem lies in the ability to automate and rapidly assess the audit requirements of Wemaps, approving those that require audit and promptly releasing those that do not. This study aims to establish an automated Wemaps audit assessment model using convolutional neural networks and transfer learning methods. By doing so, the burden of map audit can be reduced, and dissemination efficiency can be improved. The main contributions of this study are as follows: (1) Establishment of a dataset for assessing Wemaps audit requirements. (2) Utilization of VGG16 and ResNet50 neural network models for assessing Wemaps audit requirements; (3) Development of an optimal Wemaps audit assessment model through various experiments and training methods. (4) Analysis of factors influencing audit assessments based on measurement indicators and visualized results of the model. The experiments demonstrate that this method achieves high accuracy and can provide assessment services for public map audit requirements.

Yu, Zhixin; Zhou, Chen; Li, Manchun

doi: 10.1080/15230406.2023.2272660pmid: N/A

This study presents an asynchronous parallel strategy coordinating central processing unit (CPU) and graphic processing unit (GPU) to accelerate neighborhood operation (NO). Specifically, we propose a data partitioning method called multi-anchor task queuing and a task scheduling method called bi-direction task scheduling, which can support CPU and GPU to find the responsible data blocks rapidly and concurrently handle their tasks via a bi-direction merge. Moreover, we optimize the organization of threads distributed among the CPU and GPU. Experimental results show that when a 1.7 GB raster dataset is processed, the speedup ratio achieved by the proposed parallel algorithm reaches 29.63, which is 19% and 18% higher than those of the GPU and standard asynchronous parallel algorithm, respectively. Additionally, the load balance index is below 0.085, which is significantly better than the value achieved by a conventional algorithm. Thus, the strategy achieves a higher speedup ratio and more adaptable load balance, thereby accelerating the NO more efficiently. Further, the impacts of the data volume, computational intensity, organization mode of the GPU threads, and granularity of the GPU stream on the parallel efficiency are evaluated and discussed. We also test the efficiency of four other common NOs with our strategy.