City Walking Tour Videos(CWTVs)are a novel source of Volunteered Geographic Information providing street-level imagery through video sharing platforms such as YouTube.We demonstrate that these videos contain rich info...City Walking Tour Videos(CWTVs)are a novel source of Volunteered Geographic Information providing street-level imagery through video sharing platforms such as YouTube.We demonstrate that these videos contain rich information for urban analytical applications,by conducting a mobility study.We detect transport modes with a focus on active(pedestrians and cyclists)and motorised mobility(cars,motorcyclists and trucks).We chose the City of Paris as our area of interest given the rapid expansion of the bicycle network as a response to the Covid-19 pandemic and compiled a video corpus encompassing more than 66 hours of video footage.Through the detection of street names in the video and placename containing timestamps we extracted and georeferenced 1169 locations at which we summarise the detected transport modes.Our results show high potential of CWTVs for studying urban mobility applications.We detected significant shifts in the mobility mix before and during the pandemic as well as weather effects on the volumes of pedestrians and cyclists.Combined with the observed increase in data availability over the years we suggest that CWTVs have considerable potential for other applications in the field of urban analytics.展开更多
变电站屋面中的保温层、防水层、隔离层、隔汽层和找平层是房屋关键的结构和组成部分,这些层出现的不同表面缺陷,对变电站的性能、使用寿命和人员安全至关重要。本研究基于最优传输分配改进“你仅看一次”(you only look once,YOLOv5s)...变电站屋面中的保温层、防水层、隔离层、隔汽层和找平层是房屋关键的结构和组成部分,这些层出现的不同表面缺陷,对变电站的性能、使用寿命和人员安全至关重要。本研究基于最优传输分配改进“你仅看一次”(you only look once,YOLOv5s)算法来对这些层的表面缺陷进行目标检测,提出了一种更准确和更高效的解决方案,最优传输分配算法通过优化标签分配,提供了比传统阈值方法更精确的匹配,并平衡了正负样本的学习。实验结果表明,最优传输分配优化后的YOLOv5s算法在房屋缺陷的目标检测中能够更全面地考虑图片信息和学习图形特征,减少了定位损失、目标损失和分类损失。此外,最优传输分配还能够提升精确率、召回率和平均准确率(mean average precision,MAP),表明模型的预测准确性、完整性和整体性能得到了改善。因此,使用YOLOv5s算法结合最优传输分配优化的方法对变电站屋面缺陷进行目标检测具有重要的实际应用价值。展开更多
基金supported by the Swiss National Science Foundation project EV A-VGI 2[grant number 186389].
文摘City Walking Tour Videos(CWTVs)are a novel source of Volunteered Geographic Information providing street-level imagery through video sharing platforms such as YouTube.We demonstrate that these videos contain rich information for urban analytical applications,by conducting a mobility study.We detect transport modes with a focus on active(pedestrians and cyclists)and motorised mobility(cars,motorcyclists and trucks).We chose the City of Paris as our area of interest given the rapid expansion of the bicycle network as a response to the Covid-19 pandemic and compiled a video corpus encompassing more than 66 hours of video footage.Through the detection of street names in the video and placename containing timestamps we extracted and georeferenced 1169 locations at which we summarise the detected transport modes.Our results show high potential of CWTVs for studying urban mobility applications.We detected significant shifts in the mobility mix before and during the pandemic as well as weather effects on the volumes of pedestrians and cyclists.Combined with the observed increase in data availability over the years we suggest that CWTVs have considerable potential for other applications in the field of urban analytics.
文摘变电站屋面中的保温层、防水层、隔离层、隔汽层和找平层是房屋关键的结构和组成部分,这些层出现的不同表面缺陷,对变电站的性能、使用寿命和人员安全至关重要。本研究基于最优传输分配改进“你仅看一次”(you only look once,YOLOv5s)算法来对这些层的表面缺陷进行目标检测,提出了一种更准确和更高效的解决方案,最优传输分配算法通过优化标签分配,提供了比传统阈值方法更精确的匹配,并平衡了正负样本的学习。实验结果表明,最优传输分配优化后的YOLOv5s算法在房屋缺陷的目标检测中能够更全面地考虑图片信息和学习图形特征,减少了定位损失、目标损失和分类损失。此外,最优传输分配还能够提升精确率、召回率和平均准确率(mean average precision,MAP),表明模型的预测准确性、完整性和整体性能得到了改善。因此,使用YOLOv5s算法结合最优传输分配优化的方法对变电站屋面缺陷进行目标检测具有重要的实际应用价值。
