帮助计算机理解地理范围内的活动限制
Helping Computers Understand Geographically-Bound Activity Restrictions
摘要：
由于缺乏某些类型的地理数据，在一些重要领域无法开发位置感知技术。一种这样类型的“未映射的”地理数据是空间使用规则（SUR），其被定义为地理上绑定的活动限制（例如，“禁止狗”、“禁止吸烟”、“禁止钓鱼”、“禁止滑板”）。人机交互领域的研究人员最近已经开始开发用于自动绘制SUR的技术，目的是支持活动规划系统（例如，一键“我可以在这里吸烟吗？“应用程序、SUR感知度假计划工具）。在本文中，我们提出了一种新的SUR映射技术- SPtP -的性能优于30%的最先进的方法的SUR映射管道的最重要的组成部分之一：相关联的一个点观察的SUR（例如，“禁止吸烟”的标志）与相应的多边形，其中SUR适用（例如，附近的公园或整个校园的标志位于）。本文还提供了一系列新的SUR基准数据集，以帮助在这一领域的进一步研究。
英文摘要：
The lack of certain types of geographic data prevents the development of location-aware technologies in a number of important domains. One such type of "unmapped" geographic data is space usage rules (SURs), which are defined as geographically-bound activity restrictions (e.g. "no dogs", "no smoking", "no fishing", "no skateboarding"). Researchers in the area of human-computer interaction have recently begun to develop techniques for the automated mapping of SURs with the aim of supporting activity planning systems (e.g. one-touch "Can I Smoke Here?" apps, SUR-aware vacation planning tools). In this paper, we present a novel SUR mapping technique - SPtP - that outperforms state-of-the-art approaches by 30% for one of the most important components of the SUR mapping pipeline: associating a point observation of a SUR (e.g. a 'no smoking' sign) with the corresponding polygon in which the SUR applies (e.g. the nearby park or the entire campus on which the sign is located). This paper also contributes a series of new SUR benchmark datasets to help further research in this area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
