The inspection of the Public Right of Way (PROW) for accessibility barriers is necessary for monitoring and maintaining the built environment for communities' walkability, rollability, safety, active transportation, and sustainability. However, an inspection of the PROW, by surveyors or crowds, is laborious, inconsistent, costly, and unscalable. The core of smart city developments involves the application of information technologies toward municipal assets assessment and management. Sidewalks, in comparison to automobile roads, have not been regularly integrated into information systems to optimize or inform civic services. We develop an Open Automated Sidewalks Inspection System (OASIS), a free and open-source automated mapping system, to extract sidewalk network data using mobile physical devices. OASIS leverages advances in neural networks, image sensing, location-based methods, and compact hardware to perform sidewalk segmentation and mapping along with the identification of barriers to generate a GIS pedestrian transportation layer that is available for routing as well as analytic and operational reports. We describe a prototype system trained and tested with imagery collected in real-world settings, alongside human surveyors who are part of the local transit pathway review team. Pilots show promising precision and recall for path mapping (0.94, 0.98 respectively). Moreover, surveyor teams' functional efficiency increased in the field. By design, OASIS takes adoption aspects into consideration to ensure the system could be easily integrated with governmental pathway review teams' workflows, and that the outcome data would be interoperable with public data commons.

翻译：对公共通行权（PROW）进行无障碍性巡检，是监测和维护建成环境以促进社区步行性、轮椅通行性、安全性、主动交通与可持续发展的重要措施。然而，由测量员或众包方式开展的PROW巡检存在劳动强度大、一致性差、成本高昂且难以扩展的问题。智慧城市发展的核心在于将信息技术应用于市政资产评估与管理。相较于机动车道，人行道尚未被系统性地纳入信息系统以优化或指导市政服务。我们开发了开放式自动化人行道巡检系统（OASIS），这是一套免费开源的自动化制图系统，通过移动物理设备提取人行道网络数据。OASIS利用神经网络、图像传感、基于位置的方法及紧凑型硬件等前沿技术，执行人行道分割与制图，并同步识别障碍物，从而生成可用于路径规划及分析与运营报告的GIS行人交通层。我们描述了一个原型系统，该系统基于真实场景采集的影像进行训练与测试，并与地方交通路径审查团队中的人类测量员协同工作。试点实验表明，该系统在路径制图方面具有优异的精确率（0.94）和召回率（0.98）。此外，测量员团队的外业工作效率显著提升。通过设计，OASIS充分考虑了采纳性因素，确保系统能轻松融入政府路径审查团队的工作流程，且输出数据可与公共数据资源库实现互操作。