ABSTRACT

Determining the location of a building’s entrance is crucial to location-based services, such as wayfinding for pedestrians. Unfortunately, entrance information is often missing from current mainstream map providers such as Google Maps. Frequently, automatic approaches for detecting building entrances are based on street-level images that are not widely available. To address this issue, we propose a more general approach for inferring the main entrances of public buildings based on the association between spatial elements extracted from OpenStreetMap. In particular, we adopt three binary classification approaches, weighted random forest, balanced random forest, and smooth-boost to model the association relationship. There are two types of features considered in the classification: intrinsic features derived from building footprints and extrinsic features derived from spatial contexts, such as roads, green spaces, bicycle parking areas, and neighboring buildings. We conducted extensive experiments on 320 public buildings with an average perimeter of 350 m. The experimental results showed that the locations of building entrances estimated by the weighted random forest and balanced random forest models have a mean linear distance error of 21 m and a mean path distance error of 22 m, ruling out 90% of the incorrect locations of the main entrance of buildings.

摘要

确定建筑物入口的位置对于基于位置的服务（如行人寻路）至关重要。不幸的是，当前的主流地图提供商（例如Google Maps）经常缺少入口信息。通常，用于检测建筑物入口的自动方法是基于尚未广泛使用的街道级图像。为了解决这个问题，我们提出了一种更通用的方法，根据从OpenStreetMap提取的空间元素之间的关联来推断公共建筑的主要入口。特别地，我们采用三种二元分类方法，即加权随机森林，平衡随机森林和平滑提升来对关联关系进行建模。分类中考虑了两种类型的功能：源自建筑物足迹的固有特征和源自空间背景的外部特征，例如道路，绿地，自行车停放区和邻近建筑物。我们对320座平均周长为350 m的公共建筑进行了广泛的实验。实验结果表明，加权随机森林和平衡随机森林模型估计的建筑物入口位置的平均线性距离误差为21 m，平均路径距离误差为22 m，排除了90％的不正确位置。建筑物的正门。