作为地图制作者和地图使用者的机器:需要思考的关键问题?

Nina Polous
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摘要

摘要本文探讨了制图学和机器人制图的融合,解决了随着机器越来越多地同时充当地图绘制者和地图用户而出现的关键挑战和机遇。作者研究了三个关键问题:(1)如何最好地为机器表示地理数据和地图,(2)机器应该随时获得关于我们环境的哪些动态信息,以及(3)自治实体应该考虑哪些伦理、宗教和文化规范。通过解决这些问题,作者旨在促进自治系统的地理空间数据表示、管理和分析的发展,同时确保与人类和谐共处。在本文的范围内,作者试图提出一种方法,通过跨学科合作、联合研究小组以及共同标准和框架的发展,建立共同的基础,弥合传统制图与作为用户和制造者的机器的新兴需求之间的差距。在本提案中,特别是通过使用设计思维方法,事件映射原则作为一种将空间信息表示为事件的方法,被强调为一种有前途的整合静态和动态空间信息的通用框架。因为,基于事件的映射和模型可以改善机器地理数据的表示,使它们能够更好地理解环境,并在复杂和动态的上下文中做出明智的决策。这种融合最终将改变我们对机器时代地图和地理信息系统的看法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machines as mapmakers and map users: key questions to ponder upon?
Abstract. This article explores the convergence of cartography and robotic mapping, addressing key challenges and opportunities that arise as machines increasingly serve as both mapmakers and map users. The author investigates three critical questions: (1) how to best represent geographical data and maps for machines, (2) what dynamic information about our environment should be made readily available to machines, and (3) which ethical, religious, and cultural norms should be considered for autonomous entities. By addressing these questions, the author aims to facilitate the development of geospatial data representation, management, and analysis for autonomous systems, while ensuring harmonious coexistence with humans. In the scope of this paper, author tries to propose an approach to bridge the gap between traditional cartography and the emerging needs of machines as user and makers by building common ground through cross-disciplinary collaboration, joint research groups, and the development of common standards and frameworks. In this proposal, particularly by using design thinking approach, an event-mapping principle, as an approach that represents spatial information as events, is highlighted as a promising common framework for integrating static and dynamic spatial information. Since, event-based mapping and models can improve the representation of geographical data for machines, enabling them to better understand the environment and make informed decisions in complex and dynamic contexts. This convergence will ultimately transform the way we think about maps and geographical information systems in the age of machines.
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