Tagging the main entrances of public buildings based on OpenStreetMap and binary imbalanced learning

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.

确定建筑物入口位置对于诸如行人寻路导航等基于位置的服务至关重要。遗憾的是，谷歌地图（Google Maps）等当前主流地图服务商通常未收录建筑物入口信息。现有建筑入口检测自动化方法往往依赖普及度有限的街景图像。为解决这一问题，我们提出一种更通用的方法，基于从开放街道地图（OpenStreetMap）中提取的空间元素关联关系，推断公共建筑的主入口位置。具体而言，我们采用三种二分类方法——加权随机森林（weighted random forest）、平衡随机森林（balanced random forest）和平滑提升（smooth-boost）——对该关联关系进行建模。本次分类实验共考量两类特征：一类是从建筑占地面积轮廓（building footprints）中提取的固有特征，另一类是从道路、绿地、自行车停放区及周边建筑等空间环境中提取的外部特征。我们在320栋平均周长为350米的公共建筑上开展了大规模实验。实验结果表明，加权随机森林与平衡随机森林模型所预估的建筑入口位置，其平均直线距离误差为21米，平均路径距离误差为22米，可排除90%的主入口错误候选位置。