基于街景影像和深度学习技术的城市流动商贩空间分布制图

Mapping of Spatial Distribution of Street Vendors Based on Street-View Images and Deep-Learning Technology

流动商贩是城市社会生态系统中不可或缺的组成部分,高效治理流动商贩问题需要全面调查他们的经营规模和空间聚集信息。然而,传统方法在大规模流动商贩信息(尤其是他们的空间分布)的自动调查存在不足。文章提出一种基于街景影像和深度学习目标识别模型的流动商贩空间分布自动调查方法。按城市路网的固定间隔距离采集街景影像,通过人机交互的方式选取1957张包含一个或以上商贩的图像建立流动商贩标签数据。构建基于YOLO v4深度神经网络的图像目标检测模型识别街景影像库中的流动商贩,模型的平均F1值为0.77、mAP为0.67。精度能满足覆盖城市主要道路的流动商贩数量和位置调查的需要,进而应用核密度分布模型评估流动商贩的空间分布格局。以广州市的街头流动商贩为案例,通过所建立的自动调查模型在3339062幅街景影像中识别出26119名街头商贩,结果表明,流动商贩在中心城区以多中心聚集模式分布,主要集中在地铁站、城中村附近等人流量大的区域,随着道路等级的下降其数量上升,而且流动商贩偏好分布于租金中等的地区。文章提出的方法有助于实现高效、低成本和城市尺度的街头摊贩分布制图,所得结果有助于制定和实施非正规经济的空间治理政策,并进一步为街景图像丰富且开放的城市的空间治理政策的改进和实施提供建议。识别结果可用于对从业者的区位偏好分析、“邻避效应”探究以及疏导区的划定提供决策参考依据。

Street vendors are an indispensable part of the urban social ecosystem,but due to a lack of comprehensive understanding,many cities have adopted simple eviction policies,resulting in the gradual marginalization and stigmatization of the street economy.The efficient governance of street vendors requires the comprehensive investigation of their business scale and spatial distribution information.However,traditional methods have limitations in terms of automatically surveying large-scale street vendor information,particularly spatial distribution.This paper proposes a method for the automatic investigation spatial distribution of street vendors based on street-view images and a deep-learning object recognition model.Street-view images were collected at fixed intervals according to the urban road network,and 1,957 images containing one or more vendors were selected through human-machine interaction to establish street vendor label data.To achieve high recognition model accuracy,the category labels were subdivided into four categories:ground stalls,table stalls,tricycle stalls,and small truck stalls,based on the goods carriers used by street vendors.A deep neural-networkbased image object detection model based on YOLO v4 was constructed to identify street vendors in the streetview image library,with an average F1 value of 0.77 and an mAP of 0.67.The accuracy of the model was satisfactory for investigating the number and location of street vendors covering the main roads in the city and then applying a kernel density distribution model to evaluate the spatial distribution pattern of street vendors.Using street vendors in Guangzhou as a case study,the proposed automatic investigation model identified 26,119 street vendors from 3,339,062 street-view images.The results showed that the street vendors were distributed in a multicenter aggregation pattern in the central urban area,mainly concentrated in areas with high pedestrian traffic,such as subway stations and urban villages;their numbers increased as road grades decreases.Street vendors were mainly distributed in areas with medium rents.The proposed method is helpful for performing the efficient,low-cost,and city-scale mapping of street vendors;the results obtained provide suggestions for formulating and implementing spatial governance policies for the informal economy and further provide suggestions for improving and implementing spatial governance policies for open and diverse urban street-view images.The results can be used as a reference for the location preference analysis of practitioners,the exploration of NIMBY syndrome,and the determination of the formalization zone.Although street-view images have an insufficient spatiotemporal coverage,using them to perform street vendor investigations is a low-cost and efficient method compared with the use of traditional investigation methods and data sources.In addition,the method proposed in this article can be coupled with multitask deep learning algorithms to investigate additional dimensions of street vendor information,such as the sex,age,and type of business of street vendors.Relevant research needs to be conducted in the future.