CitySurfaces：城市尺度人行道材料的语义分割
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials
摘要：
虽然设计可持续和有复原力的城市建筑环境在世界各地得到越来越多的推广，但重大的数据缺口使得对紧迫的可持续性问题的研究具有挑战性。众所周知，路面具有很强的经济和环境影响;然而，由于数据汇集成本高昂且耗时，大多数城市缺乏路面的空间目录。计算机视觉的最新进展以及街道级图像的可用性，为城市以更低的实施成本和更高的准确性提取大规模建筑环境数据提供了新的机会。在本文中，我们提出了CitySurfaces，一个基于主动学习的框架，利用计算机视觉技术，使用广泛可用的街道级图像对人行道材料进行分类。我们在纽约市和波士顿的图像上训练了框架，评估结果显示mIoU得分为90.5%。此外，我们使用来自六个不同城市的图像对框架进行了评估，证明它可以应用于具有不同城市结构的地区，即使在训练数据的范围之外。CitySurfaces可以为研究人员和城市机构提供一种低成本、准确和可扩展的方法来收集人行道材料数据，这些数据在解决气候变化和地表水管理等重大可持续性问题方面发挥着关键作用。
英文摘要：
While designing sustainable and resilient urban built environment is increasingly promoted around the world, significant data gaps have made research on pressing sustainability issues challenging to carry out. Pavements are known to have strong economic and environmental impacts; however, most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data with lower implementation costs and higher accuracy. In this paper, we propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials using widely available street-level images. We trained the framework on images from New York City and Boston and the evaluation results show a 90.5% mIoU score. Furthermore, we evaluated the framework using images from six different cities, demonstrating that it can be applied to regions with distinct urban fabrics, even outside the domain of the training data. CitySurfaces can provide researchers and city agencies with a low-cost, accurate, and extensible method to collect sidewalk material data which plays a critical role in addressing major sustainability issues, including climate change and surface water management.
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。
