用AI引导标注，分析印度昌迪加尔40公里人行道无障碍问题。
Towards Human-AI Accessibility Mapping in India: VLM-Guided Annotations and POI-Centric Analysis in Chandigarh
- 引入视觉语言模型动态调整标注任务指引，提升标注效率。
- 在3个不同功能区审计40公里道路，发现1644处需改善的无障碍设施点。
- 适合城市规划、无障碍设计及人机协作研究者参考。
Project Sidewalk 是一个基于网络的平台，通过虚拟行走谷歌街景实现城市级人行道无障碍性众包标注，已在全球40个城市应用，包括美国、墨西哥、智利和欧洲。本文介绍将该工具适配至印度昌迪加尔的实践，包括调整标注类型、提供示例，并集成基于视觉语言模型（VLM）的任务引导机制，可根据街景图像与元数据动态优化指令。三位标注员评估显示，AI任务引导平均得分为4.66。利用此改进版工具，我们对昌迪加尔三个用地性质迥异的区域（住宅、商业、机构）开展以兴趣点（POI）为中心的无障碍分析，覆盖约40公里人行道和230个兴趣点，共识别出2,913个位置中1,644处可通过基础设施改进提升无障碍水平。
原文摘要 · Abstract (English)
Project Sidewalk is a web-based platform that enables crowdsourcing accessibility of sidewalks at city-scale by virtually walking through city streets using Google Street View. The tool has been used in 40 cities across the world, including the US, Mexico, Chile, and Europe. In this paper, we describe adaptation efforts to enable deployment in Chandigarh, India, including modifying annotation types, provided examples, and integrating VLM-based mission guidance, which adapts instructions based on a street scene and metadata analysis. Our evaluation with 3 annotators indicates the utility of AI-mission guidance with an average score of 4.66. Using this adapted Project Sidewalk tool, we conduct a Points of Interest (POI)-centric accessibility analysis for three sectors in Chandigarh with very different land uses, residential, commercial and institutional covering about 40 km of sidewalks. Across 40 km of roads audited in three sectors and around 230 POIs, we identified 1,644 of 2,913 locations where infrastructure improvements could enhance accessibility.
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