# DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps

arXiv: https://arxiv.org/abs/2411.18073 · HTML: https://mlsanity.com/paper/2411.18073/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 49 references that are in the map.
- First posted on arXiv 27 Nov 2024.

## Builds on (most cited first, 5 of 5)

- [A Deep Look into Neural Ranking Models for Information Retrieval](https://mlsanity.com/paper/1903.06902/) (2019)
- [New Ideas and Trends in Deep Multimodal Content Understanding: A Review](https://mlsanity.com/paper/2010.08189/) (2020)
- [GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps](https://mlsanity.com/paper/2108.09104/) (2021)
- [Understanding the Impact of the COVID-19 Pandemic on Transportation-related Behaviors with Human Mobility Data](https://mlsanity.com/paper/2202.12264/) (2022)
- [HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps](https://mlsanity.com/paper/2409.03504/) (2024)

## Built on by (most cited first, 5 of 5)

- [A Concise Survey on Lane Topology Reasoning for HD Mapping](https://mlsanity.com/paper/2504.01989/) (2025)
- [Video-based Traffic Light Recognition by Rockchip RV1126 for Autonomous Driving](https://mlsanity.com/paper/2503.23965/) (2025)
- [DuIVRS-2: An LLM-based Interactive Voice Response System for Large-scale POI Attribute Acquisition](https://mlsanity.com/paper/2605.17900/) (2026)
- [A Benchmark for Vision-Centric HD Mapping by V2I Systems](https://mlsanity.com/paper/2503.23963/) (2025)
- [GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims](https://mlsanity.com/paper/2609.05761/) (2026)

## Abstract (arXiv)

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.