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REVIEW 4 major objections 4 minor 47 references

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

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that verifying whether a photographed storefront exists in a database of billions of map locations can be done by embedding the signboard image and coordinates into one vector space and searching by approximate nearest…

desk verdict DuMapper II's 50x throughput is plausible and backed by online A/B; the offline SR@K numbers, however, are not shown to hold at billion-scale and the accuracy/throughput trade-off is under-reported. read the letter →

arxiv 2411.18073 v1 pith:R77XLS6F submitted 2024-11-27 cs.AI cs.IR

Miao Fan , Jizhou Huang , Haifeng Wang This is my paper · ORCID
classification cs.AIcs.IR
keywords pointofinterestverificationstreet-viewimagerydeepmultimodalembeddingapproximatenearestneighborsearchwebmappingservicesPOIdatabasegeohashtripletloss
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that point-of-interest (POI) verification in a large commercial map database can be automated using street-view photographs of signboards. The proposed system, DuMapper II, encodes a signboard image together with the photo's coordinates into a low-dimensional vector and finds the matching archived POI by approximate nearest neighbor search, rather than comparing thousands of candidates one by one. The authors report that this raises verification throughput 50-fold over the earlier DuMapper I pipeline while keeping online accuracy a few points below human expert mappers. If the claim holds, map platforms could replace a large share of their paid crowdsourcing verification labor with an automated system; the paper reports 405 million production verifications over 3.5 years, roughly the workload of 800 expert mappers.

What carries the argument

The load-bearing object is the deep multimodal embedding of a POI. A convolutional network extracts a feature matrix from the signboard image, a geospatial hash of the coordinates is turned into learned embeddings, and two cross-attention layers fuse these feature sets before average pooling and concatenation form a single vector. Training uses triplet loss with a margin so that street-view queries and archived POIs land in one shared space; once that space is built, an approximate nearest neighbor index over the archived vectors replaces the earlier pipeline's geo-spatial index, OCR, and candidate ranking.

What would settle it

Re-run the released DuMapper II code with the same test queries but a search space that includes all archived POI embeddings (or a large random sample), and compare success-at-rank-K against the reported 88.08%, 93.17%, and 95.12%; if the numbers drop materially, the accuracy claim is an artifact of the small pool.

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Extended reading notes

Core claim

The central claim is that POI verification can be reformulated as nearest-neighbor search in a learned multimodal embedding space. DuMapper II takes a signboard image and the coordinates at which it was photographed, fuses the two modalities with a cross-attention network, and maps the result to a low-dimensional vector; all archived POIs are pre-indexed in this same space. An approximate nearest neighbor index then returns the closest archived POI in milliseconds. The authors report offline success rates of 88.08% at rank 1 for DuMapper II and 91.74% for a reranked variant DuMapper II*, compared with 78.42% for DuMapper I; online, DuMapper II reaches 152.49 queries per second versus 3.02 for DuMapper I, a 50-fold throughput gain, with accuracy 85.14% versus 94.52% for human expert mappers.

Load-bearing premise

The offline accuracy numbers depend on the candidate pool the search ran over, and the paper does not say whether that pool was the full billion-entry database or just the 12,000 test POIs.

Editorial extensions

If this is right

  • Automatic verification can run at production scale: at 152.49 queries per second, the system can absorb daily millions of street-view submissions that previously required thousands of paid mappers.
  • Accuracy and throughput can be traded explicitly: DuMapper II* restricts the ANN to the ten nearest neighbors and reranks them with the multimodal ranking module, raising offline SR@1 from 88.08% to 91.74% while cutting throughput to 7.38 QPS.
  • Because all archived POIs are embedded once and indexed, a verification query costs only a vector lookup, so checking a point against billions of entries takes milliseconds rather than a scan.
  • The future-work section extends the same infrastructure to POI addition, deletion, and attribute updates, suggesting a single multimodal embedding could maintain the whole database.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: The paper does not state whether offline SR@K was measured against the full archived POI set or only the 12,000 test POIs; if the smaller pool was used, the reported 88–91% success rates may not reproduce at true billion-scale search. This is the assumption most worth testing before relying on the accuracy numbers.
  • Inference: The 50x figure compares DuMapper II to DuMapper I; a deployment that needs the higher accuracy of DuMapper II* would gain far less in throughput, so the speedup claim is specific to the no-reranking variant.
  • Inference: A natural extension is to report recall versus the size of the candidate pool and to separate the quality of the learned embedding from the quality of the ANN index by comparing exact nearest neighbor against ANN results on the same queries.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper describes DuMapper, an automatic POI verification system deployed at Baidu Maps. DuMapper I mimics expert mappers via a three-stage pipeline (geo-spatial index, OCR, and candidate POI ranking). DuMapper II replaces this pipeline with a deep multimodal embedding that fuses signboard images and coordinates, followed by approximate nearest neighbor (ANN) search over the archived POI embedding space. Offline experiments report SR@K on a 12,000-POI test set, and online A/B tests measure accuracy and QPS for five frameworks: expert mappers, DuMapper I, DuMapper I*, DuMapper II, and DuMapper II*. The paper claims DuMapper II increases throughput by 50 times over DuMapper I, and that DuMapper II* achieves 90.89% online accuracy close to the 94.52% expert baseline. It also reports production deployment since June 2018 and 405 million verification iterations by December 2021.

Significance. If the reported results are valid, DuMapper II represents a substantial industrial advance: an end-to-end learned embedding plus ANN search that automates POI verification at a scale of billions of archived POIs, with large labor savings. The paper deserves credit for conducting online A/B tests on real production traffic, for reporting deployment statistics over 3.5 years, and for releasing the source code of DuMapper II. These elements give the throughput claim a degree of external validity that pure offline evaluations would lack. However, the significance is tempered by the lack of a specified retrieval corpus for the offline SR@K metric, the absence of statistical confidence measures, and the fact that the headline 50x throughput figure corresponds to the lower-accuracy configuration rather than the configuration that approaches expert-level accuracy.

major comments (4)
  1. [5.2.2, Table 2] The retrieval corpus used to compute the offline SR@K values is never specified. The text says the task is to find the exact POI 'through the large-scale POI database,' but it does not state whether the candidate set for each query is the full billion-POI database, a geo-spatially restricted subset, or only the 12,000 test POIs from Table 1. If the candidate set is the test set itself, the reported SR@1 of 88.08% for DuMapper II is a near-duplicate retrieval result over a small closed set and does not support the abstract's claim of a 'more accurate search through billions' of archived POIs. Please state explicitly the size and composition of the search space for each row of Table 2, and if the full database was used, describe how the ground truth was defined and how the search was constrained.
  2. [5.3.3, Table 3] The headline claim that DuMapper II 'significantly increase[s] the throughput of automatic POI verification by 50 times' applies to the configuration with online accuracy of 85.14%, which is 9.38 percentage points below the expert mapper baseline of 94.52%. The configuration that achieves accuracy within a few points of experts, DuMapper II*, has QPS of 7.38, only about 2.4x DuMapper I. The paper should separate the accuracy and throughput trade-offs for these two configurations and should not present the 50x figure as characteristic of the system that approaches expert-level accuracy. This distinction is central to interpreting the practical value of the contribution.
  3. [5.2.3, Table 2] The offline results are reported without error bars, confidence intervals, or significance tests. In particular, the SR@1 difference between DuMapper II (88.08%) and DuMapper I* (87.66%) is only 0.42 percentage points, which may be within sampling noise given the test set of 73,218 queries. Reporting bootstrap confidence intervals or a paired significance test would clarify whether the observed differences among the automatic frameworks are reliable and would make the accuracy comparisons in the paper more credible.
  4. [4.1.2, Eqs. (4)-(5)] No ablation isolates the contribution of the cross-attention fusion used in the deep multimodal embedding. Since the paper's novelty rests on this fusion, the authors should compare it against simpler alternatives, such as concatenating average-pooled CNN features with the GeoHash embedding, to demonstrate that the cross-attention mechanism is load-bearing for the reported SR@K improvements. Without such an ablation, it is unclear whether the gains come from the fusion architecture or from the overall embedding and ANN search scheme.
minor comments (4)
  1. [4.1.2, Eq. (9)] The triplet loss uses cosine similarity but the notation could be clarified by explicitly stating that the norm in the denominator is the L2 norm. Also, the negative sampling strategy is described only as 'randomly sampled from other POIs'; please specify whether any hard-negative mining is used, since random negatives can lead to trivial embeddings and affect reproducibility.
  2. [5.3.3, Table 3] The statement that DuMapper II* 'can still be at least doubled' in efficiency is imprecise; Table 3 shows QPS of 7.38 versus 3.02 for DuMapper I, which is about 2.44x. The sentence should be revised to state the observed multiple directly.
  3. [Figure 4] The schematic in Figure 4 labels Q, K, and V but does not indicate the role of the projection matrices W and U used in Eqs. (4) and (5). Adding the matrix names to the figure or to its caption would help readers connect the diagram to the equations.
  4. [5.1.1 and 5.3.1] The online A/B testing description says the new framework serves 10% of traffic for at least one week, but it does not report the number of queries or the time span for each row of Table 3. Reporting these details would help assess the stability of the QPS and accuracy measurements.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the reported SR@K numbers are held-out retrieval results and the 50x throughput claim is a measured production A/B comparison.

full rationale

The paper's central derivation chain is self-contained and empirically grounded. DuMapper II's deep multimodal embedding is trained with a triplet loss on a training subset (Table 1, Eq. 9) and evaluated on a held-out test subset whose POIs are completely different from those in training and validation; the offline SR@K metrics in Table 2 are therefore genuine held-out retrieval measurements, not quantities defined by a fitted parameter or by construction. The online A/B test (Table 3) compares deployed systems on real street-view traffic using accuracy and QPS; the 50x throughput figure is the ratio of measured QPS values (152.49 / 3.02), not a derived constant. The accuracy/throughput trade-off between DuMapper II and DuMapper II* is explicitly discussed, so the higher-accuracy variant's lower speed is disclosed rather than hidden. Self-citations appear only as references to prior Baidu systems and related work; none is used as a load-bearing uniqueness theorem or as justification for an otherwise unsupported ansatz. The reader's concern that the offline candidate pool for SR@K is unspecified is a reporting gap and a threat to the comparability of the offline numbers, but it is not a circularity: the metric is not defined in terms of any fitted parameter or of the claimed speedup. No step in the paper reduces, by its own equations or by self-citation, to its own inputs.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on supervised learning assumptions and an unstated offline evaluation setup. The only invented entity is the DuMapper system itself, which is an engineered artifact rather than a new physical or mathematical entity.

free parameters (4)
  • margin gamma = not reported
    Hyperparameter in the triplet loss (Eq. 9) controlling the separation between positive and negative pairs; chosen by hand.
  • embedding dimensions (l, d) = not reported
    Dimensions of the intermediate feature matrices and output embeddings in the cross-attention DME (Eqs. 1-8); chosen network sizes.
  • number of nearest neighbors k = 10
    Set to 10 in DuMapper II* to trade off ANN accuracy against re-ranking cost (Section 5.1.5).
  • GSI radius r = not reported
    Heuristic radius for geo-spatial candidate fetching in DuMapper I (Section 3.1.2); not used in DuMapper II.
assumptions (4)
  • domain assumption Ground-truth labels in the triplet training set are correct
    The model is trained with a triplet loss (Eq. 9) assuming each signboard image belongs to the annotated POI id; label noise is not analyzed (Section 4.1.2).
  • domain assumption The offline evaluation candidate pool contains the true POI
    The paper does not state whether SR@K is computed over the full database or only the 12k test POIs; without this, retrieval difficulty is undefined (Section 5.2.2).
  • standard math ANNOY returns a tree-structured approximation of the nearest neighbor
    Section 4.2.2 relies on ANNOY's tree-based ANN search to reduce lookup complexity, assuming acceptable recall-quality trade-offs.
  • domain assumption Geohash discretization preserves locality sufficiently
    Eq. 2 maps coordinates to discrete Geohash codes; the model must learn to handle discretization boundaries, which is not discussed.

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Cite this review

Pith. "Pith review of DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps." pith.science (2026). https://pith.science/paper/R77XLS6F

@misc{pith2026241118073,
  author       = {Pith},
  title        = {Pith review of: DuMapper: Towards Automatic Verification of Large-Scale POIs with Street Views at Baidu Maps},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/R77XLS6F}},
  note         = {Machine review of arXiv:2411.18073}
}
abstract

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.

Figures

Figures reproduced from arXiv: 2411.18073 by the authors.

Figure 1
Figure 1. A screenshot of the result of a POI search at Baidu [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. DuMapper is designed to be an intelligent agent that can continuously verify POIs with the multimodal street-view [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. DuMapper I is the original automatic framework [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: DuMapper II revolutionizes the way to attain large [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]

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Works this paper leans on

47 extracted references · 45 canonical work pages

  1. [1]

    Andrea Ballatore and Jamal Jokar Arsanjani. 2019. Placing Wikimapia: An Exploratory Analysis. International Journal of Geographical Information Science 33, 8 (2019), 1633–1650

    work page 2019

  2. [2]

    Filip Biljecki and Koichi Ito. 2021. Street View Imagery in Urban Analytics and GIS: A Review. Landscape and Urban Planning 215 (2021), 104217

    work page 2021

  3. [3]

    Arindam Chaudhuri, Krupa Mandaviya, Pratixa Badelia, and Soumya K. Ghosh

  4. [4]

    Wei Chen, Weiping Wang, Li Liu, and Michael S. Lew. 2021. New Ideas and Trends in Deep Multimodal Content Understanding: A Review. Neurocomputing 426 (2021), 195–215

    work page 2021

  5. [5]

    Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu. 2021. Curriculum meta-learning for next POI recommendation. In Proceed- ings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 2692–2702

    work page 2021

  6. [6]

    Hsiu-Min Chuang and Chia-Hui Chang. 2015. Verification of POI and Location Pairs via Weakly Labeled Web Data. In Proceedings of the 24th International Conference on World Wide Web. 743–748

    work page 2015

  7. [7]

    Hsiu-Min Chuang, Chia-Hui Chang, Ting-Yao Kao, Chung-Ting Cheng, Ya-Yun Huang, and Kuo-Pin Cheong. 2016. Enabling Maps/Location Searches on Mobile Devices: Constructing a POI Database via Focused Crawling and Information Extraction. International Journal of Geographical Information Science 30, 7 (2016), 1405–1425

    work page 2016

  8. [8]

    David Coleman, Yola Georgiadou, and Jeff Labonte. 2009. Volunteered Geographic Information: The Nature and Motivation of Produsers. International journal of spatial data infrastructures research 4, 4 (2009), 332–358

    work page 2009

Show all 47 references
  1. [9]

    Risma Ekawati and Untung Suprihadi. 2018. Analysis of S2 (Spherical) Geometry Library Algorithm for GIS Geocoding Engineering. TELKOMNIKA (Telecommu- nication Computing Electronics and Control) 16, 1 (2018), 334–342

    2018

  2. [10]

    Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S. Weld. 2008. Open Information Extraction from the Web. Commun. ACM 51, 12 (dec 2008), 68–74

    2008

  3. [11]

    Hongchao Fan, Alexander Zipf, Qing Fu, and Pascal Neis. 2014. Quality assess- ment for building footprints data on OpenStreetMap. International Journal of Geographical Information Science 28, 4 (2014), 700–719

    2014

  4. [12]

    Miao Fan, Yibo Sun, Jizhou Huang, Haifeng Wang, and Ying Li. 2021. Meta- Learned Spatial-Temporal POI Auto-Completion for the Search Engine at Baidu Maps. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2822–2830

    2021

  5. [13]

    Xin Feng, Youni Jiang, Xuejiao Yang, Ming Du, and Xin Li. 2019. Computer Vision Algorithms and Hardware Implementations: A Survey. Integration 69 (2019), 309–320

    2019

  6. [14]

    Andrew J Flanagin and Miriam J Metzger. 2008. The Credibility of Volunteered Geographic Information. GeoJournal 72, 3-4 (2008), 137–148

    2008

  7. [15]

    Chillo Ga, Jeongho Lee, Won Hee Lee, and Kiyun Yu. 2013. New POI Construction with Street-Level Imagery. IEICE Transactions on Information and Systems 96, 1 (2013), 129–133

    2013

  8. [16]

    Tao Ge, Xingxing Zhang, Furu Wei, and Ming Zhou. 2019. Automatic Grammatical Error Correction for Sequence-to-Sequence Text Generation: An Empirical Study. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 6059–6064

    2019

  9. [17]

    Wael H Gomaa, Aly A Fahmy, et al. 2013. A Survey of Text Similarity Approaches. international journal of Computer Applications 68, 13 (2013), 13–18

    2013

  10. [18]

    Michael F Goodchild and Linna Li. 2012. Assuring the Quality of Volunteered Geographic Information. Spatial statistics 1 (2012), 110–120

    2012

  11. [19]

    Bruce Croft, and Xueqi Cheng

    Jiafeng Guo, Yixing Fan, Liang Pang, Liu Yang, Qingyao Ai, Hamed Zamani, Chen Wu, W. Bruce Croft, and Xueqi Cheng. 2020. A Deep Look into Neural Ranking Models for Information Retrieval. Information Processing & Management 57, 6 (2020), 102067

    2020

  12. [20]

    Mordechai Haklay and Patrick Weber. 2008. OpenStreetMap: User-Generated Street Maps. IEEE Pervasive Computing 7, 4 (2008), 12–18

    2008

  13. [21]

    Jizhou Huang, Haifeng Wang, Miao Fan, An Zhuo, and Ying Li. 2020. Personalized Prefix Embedding for POI Auto-Completion in the Search Engine of Baidu Maps. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining . 2677–2685

    2020

  14. [22]

    Jizhou Huang, Haifeng Wang, Miao Fan, An Zhuo, Yibo Sun, and Ying Li. 2020. Understanding the Impact of the COVID-19 Pandemic on Transportation-Related Behaviors with Human Mobility Data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & D...

    2020

  15. [23]

    Jizhou Huang, Haifeng Wang, and Wang Shaolei. 2022. DuIVRS: A Telephonic Interactive Voice Response System for Large-Scale POI Attribute Acquisition at Baidu Maps. In Proceedings of the 31st ACM International Conference on Informa- tion and Knowledge Management

    2022

  16. [24]

    Jizhou Huang, Haifeng Wang, Yibo Sun, Miao Fan, Zhengjie Huang, Chunyuan Yuan, and Yawen Li. 2021. HGAMN: Heterogeneous Graph Attention Matching Network for Multilingual POI Retrieval at Baidu Maps. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data...

    2021

  17. [25]

    Bin Jiang and Jean-Claude Thill. 2015. Volunteered Geographic Information: Towards the establishment of a new paradigm. Computers, Environment and Urban Systems 53 (2015), 1–3

    2015

  18. [26]

    Junglas and Richard T

    Iris A. Junglas and Richard T. Watson. 2008. Location-Based Services. Commun. ACM 51, 3 (mar 2008), 65–69

    2008

  19. [27]

    Min Gyu Kim and Soo Hong Park. 2014. Construction and Application of POI Database with Spatial Relations Using SNS. Spatial Information Research 22, 4 (2014), 21–38

    2014

  20. [28]

    K. L. Kwok. 1989. A Neural Network for Probabilistic Information Retrieval. SIGIR Forum 23, SI (may 1989), 21–30

    1989

  21. [29]

    Yann LeCun, Yoshua Bengio, et al. 1995. Convolutional Networks for Images, Speech, and Time Series. The handbook of brain theory and neural networks 3361, 10 (1995), 1995

    1995

  22. [30]

    Moore, Alexander Gray, and Ke Yang

    Ting Liu, Andrew W. Moore, Alexander Gray, and Ke Yang. 2004. An Investigation of Practical Approximate Nearest Neighbor Algorithms. In Proceedings of the 17th International Conference on Neural Information Processing Systems . 825–832

    2004

  23. [31]

    Eddy Maddalena, Luis-Daniel Ibáñez, and Elena Simperl. 2020. Mapping Points of Interest Through Street View Imagery and Paid Crowdsourcing. 11, 5, Article 63 (aug 2020), 28 pages

    2020

  24. [32]

    Jayant Madhavan, David Ko, Łucja Kot, Vignesh Ganapathy, Alex Rasmussen, and Alon Halevy. 2008. Google’s Deep Web Crawl. Proc. VLDB Endow. 1, 2 (aug 2008), 1241–1252

    2008

  25. [33]

    Bhaskar Mitra, Nick Craswell, et al. 2018. An Introduction to Neural Information Retrieval. Now Foundations and Trends

    2018

  26. [34]

    Roger Moussalli, Mudhakar Srivatsa, and Sameh Asaad. 2015. Fast and Flexible Conversion of Geohash Codes to and from Latitude/Longitude Coordinates. In 2015 IEEE 23rd Annual International Symposium on Field-Programmable Custom Computing Machines. IEEE, 179–186

    2015

  27. [35]

    Bharat Rao and Louis Minakakis. 2003. Evolution of Mobile Location-Based Services. Commun. ACM 46, 12 (dec 2003), 61–65

    2003

  28. [36]

    Rezende, Chanmi You, and Seong-Gyun Jeong

    Jérôme Revaud, Minhyeok Heo, Rafael S. Rezende, Chanmi You, and Seong-Gyun Jeong. 2019. Did It Change? Learning to Detect Point-Of-Interest Changes for Proactive Map Updates. In 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 4081–4090

    2019

  29. [37]

    Li, William G

    Timothy Sohn, Kevin A. Li, William G. Griswold, and James D. Hollan. 2008. A Diary Study of Mobile Information Needs. InProceedings of the SIGCHI Conference on Human Factors in Computing Systems . 433–442

    2008

  30. [38]

    Yibo Sun, Jizhou Huang, Chunyuan Yuan, Miao Fan, Haifeng Wang, Ming Liu, and Bing Qin. 2021. GEDIT: Geographic-Enhanced and Dependency-Guided Tagging for Joint POI and Accessibility Extraction at Baidu Maps. In Proceedings of the 30th ACM International Conference on Informatio...

    2021

  31. [39]

    Guillaume Touya, Vyron Antoniou, Ana-Maria Olteanu-Raimond, and Marie- Dominique Van Damme. 2017. Assessing Crowdsourced POI Quality: Combining Methods based on Reference Data, History, and Spatial Relations. ISPRS Interna- tional Journal of Geo-Information 6, 3 (2017), 80

    2017

  32. [40]

    Le Hong Van and Atsuhiro Takasu. 2015. An Efficient Distributed Index for Geospatial Databases. In Database and Expert Systems Applications . Springer, 28–42

    2015

  33. [41]

    Athanasios Voulodimos, Nikolaos Doulamis, Anastasios Doulamis, and Eftychios Protopapadakis. 2018. Deep Learning for Computer Vision: A Brief Review. Computational intelligence and neuroscience 2018 (2018)

    2018

  34. [42]

    Deguo Xia, Jizhou Huang, Jianzhong Yang, Xiyan Liu, and Haifeng Wang. 2022. DuARUS: Automatic Geo-object Change Detection with Street View Imagery for Updating Road Database at Baidu Maps. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management

    2022

  35. [43]

    Deguo Xia, Xiyan Liu, Wei Zhang, Hui Zhao, Chengzhou Li, Weiming Zhang, Jizhou Huang, and Haifeng Wang. 2022. DuTraffic: Live Traffic Condition Predic- tion with Trajectory Data and Street Views at Baidu Maps. In Proceedings of the 31st ACM International Conference on Informat...

    2022

  36. [44]

    Jianzhong Yang, Xiaoqing Ye, Bin Wu, Yanlei Gu, Ziyu Wang, Deguo Xia, and Jizhou Huang. 2022. DuARE: Automatic Road Extraction with Aerial Images and Trajectory Data at Baidu Maps. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 4321–4331

    2022

  37. [45]

    Lih Wei Yeow, Raymond Low, Yu Xiang Tan, and Lynette Cheah. 2021. Point-of- Interest (POI) Data Validation Methods: An Urban Case Study.ISPRS International Journal of Geo-Information 10, 11 (2021)

    2021

  38. [46]

    Honggang Zhang, Kaili Zhao, Yi-Zhe Song, and Jun Guo. 2013. Text Extraction from Natural Scene Image: A Survey. Neurocomputing 122 (2013), 310–323

    2013

  39. [2017]

    Optical Character Recognition Systems . 9–41

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Reviewed August 12, 2026 · model on record in the stance chip above.