| 地图图像智能识别与理解:特征、方法与展望 |
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| 引用本文： | 周熙然,李德仁,薛勇,汪云甲,邵振峰. 地图图像智能识别与理解:特征、方法与展望[J]. 武汉大学学报(信息科学版), 2022, 47(5): 641-650. DOI: 10.13203/j.whugis20210300 |
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| 作者姓名： | 周熙然 李德仁 薛勇 汪云甲 邵振峰 |
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| 作者单位： | 1.中国矿业大学环境与测绘学院，江苏 徐州，221116 |
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| 基金项目： | 国家自然科学基金41871260国家自然科学基金42090012国家自然科学基金41771452国家自然科学基金41771454 |
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| 摘 要： | 随着测绘制图与通讯技术的发展，公众能够借助各种平台和工具实时地自由创建、发布、编辑和共享地图图像大数据资源和产品，地图图像在地图内容、绘图标准等方面具有了显著的泛在性，导致难以创建大规模、高质量的地图图像标注数据。因此，尽管现有深度学习方法在识别标准地图的内容中取得了突破性的进展，但受制于地图图像标注数据的局限，依然无法有效应对地图图像的识别和理解。根据目前国内外的相关研究进展与挑战，结合地理空间人工智能技术，探讨支持泛源地图图像大数据识别的理论与技术框架。首先，提出既能够表达地图图像内容，又能够为模型或算法表征的地图特征；然后，探讨面向地图图像内容识别的地理空间人工智能技术，以及面向地图图像理解的语义分析方法；最后，总结和展望基于地图图像大数据的相关应用及潜在价值。需要进一步研究支持地图图像表征的理论与方法，且集成地图图像显式内容的识别（地图感知）和地图图像潜在语义的分析（地图认知）才可充分挖掘地图图像大数据的价值。希望能够从数据表征和地理空间人工智能的角度为地图图像的研究提供新思路。
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| 关 键 词： | 地理空间人工智能 地图图像识别 泛源地图 语义分析 |
| 收稿时间： | 2022-02-11 |
Intelligent Map Image Recognition and Understanding: Representative Features,Methodology and Prospects |
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| Affiliation: | 1.School of Environment and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China |
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| Abstract: | Objectives The rapid development of cartographical and communication technologies makes the public free to create, publish, edit and share their map image resources and products with various platforms and tools. These map images are remarkably ubiquitous in terms of map content, mapping standards, and other aspects, which poses a big challenge to establishing massive well-annotated map image data. Thus, although the state-of-the-art deep learning methods have made a breakthrough in recognizing the standard?ized maps, they are still intrinsically unqualified to effectively address map image recognition and understanding due to the inadequate well-labeled map images. Methods This paper summarizes the progress and challenges regarding map image recognition and discusses the theoretical configurations and potential geospatial artificial intelligence (GeoAI) techniques for efficient map image recognition and understanding. We propose the map features for deep learning models to represent map image content. Then, we explore the promising methodologies for map image content recognition and the possible semantic analysis methods for map image understanding. Subsequently, we prospect several implementations regarding map image recognition and understanding and their future potentials. Results In our opinion, further investigation on theories and methods for map image representation is essential. Moreover, fully utilizing the values of map im?ages depends upon recognizing the explicit content (map image perception) and mining the hidden semantics (map cognition). Conclusions We hope our exploration can contribute to the cartographical community offering a GeoAI and data representation integrated perspective on map image utilization. |
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