With the increased popularity of mobile devices, Web mapping services have become an indispensable tool in our daily lives. To provide user-satisfied services, such as location searches, the point of interest (POI) database is the fundamental infrastructure, as it archives multimodal information on billions of geographic locations closely related to people's lives, such as a shop or a bank. Therefore, verifying the correctness of a large-scale POI database is vital. To achieve this goal, many industrial companies adopt volunteered geographic information (VGI) platforms that enable thousands of crowdworkers and expert mappers to verify POIs seamlessly; but to do so, they have to spend millions of dollars every year. To save the tremendous labor costs, we devised DuMapper, an automatic system for large-scale POI verification with the multimodal street-view data at Baidu Maps. DuMapper takes the signboard image and the coordinates of a real-world place as input to generate a low-dimensional vector, which can be leveraged by ANN algorithms to conduct a more accurate search through billions of archived POIs in the database for verification within milliseconds. It can significantly increase the throughput of POI verification by $50$ times. DuMapper has already been deployed in production since \DuMPOnline, which dramatically improves the productivity and efficiency of POI verification at Baidu Maps. As of December 31, 2021, it has enacted over $405$ million iterations of POI verification within a 3.5-year period, representing an approximate workload of $800$ high-performance expert mappers.

翻译：随着移动设备的日益普及，网络地图服务已成为我们日常生活中不可或缺的工具。为提供用户满意的服务（例如位置搜索），兴趣点数据库是基础性设施，它归档了数十亿个与人们生活密切相关的、如商店或银行等地理位置的多模态信息。因此，验证大规模兴趣点数据库的正确性至关重要。为实现这一目标，许多工业公司采用志愿地理信息平台，使成千上万的众包工作者和专业制图员能够无缝验证兴趣点；但为此，他们每年必须花费数百万美元。为节省巨大的人力成本，我们设计了DuMapper，一个利用百度地图多模态街景数据进行大规模兴趣点验证的自动化系统。DuMapper以真实场所的招牌图像和坐标作为输入，生成一个低维向量，该向量可通过近似最近邻算法在数十亿个已归档兴趣点的数据库中进行更精确的搜索，并在毫秒级时间内完成验证。该系统可将兴趣点验证的吞吐量显著提升$50$倍。DuMapper自\DuMPOnline起已部署于生产环境，极大地提高了百度地图兴趣点验证的生产力和效率。截至2021年12月31日，该系统在三年半的时间内已执行超过$4.05$亿次兴趣点验证迭代，相当于约$800$名高性能专业制图员的工作量。