$30 off During Our Annual Pro Sale. View Details »

190717ICC2019_MCR

 190717ICC2019_MCR

* 20190717_International Cartographic Conference 2019
* The Development of Open Source Based Citizen Collaboration Applications for Infrastructure Management: My City Report

Toshikazu SETO

July 17, 2019
Tweet

More Decks by Toshikazu SETO

Other Decks in Education

Transcript

  1. The Development of Open Source Based Citizen
    Collaboration Applications for Infrastructure
    Management: My City Report
    Toshikazu Seto, Yoshihide Sekimoto, Hiroshi Omata,
    Hiroya Maeda, Takehiro Kashiyama, Shusaku HIGASHI,
    Masato Fujii, and Haruyuki SEKI.
    1
    2019/07/17 International Cartographic Conference 2019 @ Tokyo

    View Slide

  2. l Open Government
    l Incident Reporting System
    n Chiba Report n FixMyStreet n 311 Chicago
    Etc…
    Citizens can report local problems (Pothole App. 311 App.)
    u Infrastructure maintenance u ICT
    l Lack of Experts
    l Expensive Cost
    • Research for citizen feedback systems
    (Patel, 2015; Goldstein,2013; Offenhuber,2015)
    Possibility of Citizens Collaboration for
    Infrastructure Management
    Collaborate with citizen and government together !

    View Slide

  3. Background our studies…
    3
    https://doi.org/10.3390/ijgi8030115

    View Slide

  4. Technological Seeds
    • Digital transformation and automation of administrative work
    • Image process using deep learning is quite high accuracy
    • AI (artificial intelligence) for supporting daily work is starting
    4
    (0):損傷はない
    (1):修繕すべき
    損傷はない
    (2):修繕すべき
    損傷がある
    市民からの投稿画像
    点検業務による画像
    (ラベル)
    ・損傷はない
    ・修繕すべき損傷はない
    ・修繕すべき損傷がある
    Joint-research by Chiba-city(March, 2016)
    Tweet by Mayor of Chiba City

    View Slide

  5. The Progress of MCR project
    supported by NICT research grant
    FY2016 FY2017 FY2018
    ・Prototyping
    ・Demonstration
    ・Development of
    for Road Manager
    application
    ■General Meeting (3 times)
    ・4 municipalities
    (+observer 4 municipalities)
    ・Construction of MCR
    application and backend
    systems
    ・Improvement of RM
    application
    ・ Improvement of MCR
    systems
    ・Continuous pubic trial
    (Muroran and Numazu)
    ・Improvement of RM
    application
    ・Development of Road AI
    dashboard system
    Call for Organization
    ■General Meeting (6 times)
    ・5 municipalities
    (+observer 4 municipalities)
    Call for Organization
    ■General Meeting (4 times)
    ・7 municipalities

    View Slide

  6. https://www.mycityreport.jp/
    ①MCR Smartphone App.
    ②MCR Management System ④ Road Damage Detector Dashboard
    ③ Road Damage Detector
    6
    Application for citizens
    Collaboration
    Application for
    road managers
    Developed by Georepublic Japan Developed by Sekimoto Lab.

    View Slide

  7. Road Management Statistics of
    Local Government
    Name
    Total road
    length
    (km)
    Budget of
    Civil
    Engineering
    (千円)
    Budget of Civil
    management
    (千円)
    Budget of
    Road and
    bridge
    (千円)
    Employee
    Of Civil Engineer
    Civil
    Engine
    er
    Road
    Engineer
    Muroran 441.6 945,736 0 740,249 28 28 0
    Numazu 1,135.5 403,891 74 263,137 106 106 0
    Chiba 3,195.0 5,720,467 72,223 2,424,234 294 276 18
    Hanamaki 3,302.5 748,276 4,896 701,009 22 22 0
    Kaga 684.8 209,061 0 186,624 48 45 3
    Higashi
    Hiroshima
    2180.6 1,213,502 0 964,968 120 120 0
    Shinagaw
    a
    326.1 574,923 0 226,031 87 0 0
    ■Source︓
    ・Total road length(2015)︓【総務省】公共施設状況調経年⽐較表H27
    ・Budget(2016)︓【総務省】地⽅財政状況調査2017
    ・Employee(2017)︓【総務省】平成29年地⽅公共団体定員管理調査 7

    View Slide

  8. The basic function of MCR application
    Default interface Filtering mode
    Reporting
    Select of the Map Select of categories Text and photo
    Status
    confirmation
    Publish API
    of Open311
    8
    Notifications of the
    municipality

    View Slide

  9. The Interface for Municipal Managers
    Default
    Interface
    Confirmation of
    Status
    Detailed status
    management
    Printing
    Response for
    citizen
    連絡表
    受付月日 2018-08-16 担当者 室蘭全職員
    現地住所 住宅地図
    通報者氏名 dai
    連絡先 通報方法 スマホ
    602
    いつも雨の後はこのようになっていて歩けません。ご対応お願いします。
    タイトル 水たまり
    Response/
    Repair 9

    View Slide

  10. Main Reporting Category of MCR
    • Report for Problems
    – Mainly reporting issues of the infrastructure
    (decided by municipality such as road, park, river
    etc)
    • Report for Self-solving
    – Citizen reported to themselves solve the problem
    of the city (garbage picking and minor repair)
    • Specific Theme Report
    – Things that municipality side particularly calls for
    contribution (city recommendation spot, etc.)
    10
    ➔ Now, adjusting the functions for full-scale operation by multiple
    local governments. You can use only testing Report for Problems

    View Slide

  11. Feedback of MCR function development from
    participating local governments
    カテゴリ FY2016 FY2017 FY2018 Total
    Smartphone 33 150 87 270
    Administrative function 39 32 119 190
    Public mode function - 8 9 17
    Both of system - 6 9 15
    Operation - 22 - 22
    Total 72 218 224 514

    View Slide

  12. The Statistics of public trial reporting using MCR application
    periods: April 2018 to February 2019
    Category Incomplete Finished Total
    Road 58 21 79
    Park 10 6 16
    Other 3 9 12
    Category Incomplete Finished Total
    Self-solving 0 1 1
    Road 14 14 28
    Park 3 5 8
    River 0 1 1
    Tourism facilities 0 0 0
    ■Muroran City:27 users (Total 107 reports)
    ■Numazu City:14 users (Total 38 reports)
    Until the solution is initially
    demonstrated
    about from two months to
    within 1 month
    Mainly posts are not major roads,
    it is difficult to judge to repair
    10 new problem & solved in
    February

    View Slide

  13. Distribution of public trial reporting by MCR
    April 2018 to February 2019
    Incomplete
    Finished
    Observation

    View Slide

  14. Reporting Example of MCR
    Finished Incomplete Incomplete
    of Park
    •Displaced spot alone
    and remarkably
    discovered
    •Continuous damage
    •Section outside the
    jurisdiction of municipality
    •The broken of guide board
    or a bench, relatively repair
    is fast
    •The play equipment tends
    to take long to repair
    14

    View Slide

  15. ③Road Damage Detector
    15
    • Automatic judgment of damage and classification within 1-2
    seconds within smartphone
    • SSD Inception V2 / SSD MobileNet
    • Launched as an Android app with MIT License
    • https://github.com/sekilab/RoadDamageDetector

    View Slide

  16. 16

    View Slide

  17. 17
    Road damage types in RDD dataset and their definitions
    Source: Road Maintenance and Repair Guidebook 2013 (JRA, 2013) in Japan.

    View Slide

  18. 18
    Number of damage instances in each class in each municipality (FY2017)
    D00 D01 D10 D11 D20 D40 D43 D44
    Recall of SSD Inception V2 0.22 0.60 0.10 0.05 0.68 0.03 0.81 0.62
    Precision of SSD Inception V2 0.73 0.84 0.99 0.95 0.73 0.67 0.77 0.81
    Accuracy of SSD Inception V2 0.78 0.80 0.94 0.92 0.85 0.95 0.95 0.83
    Recall of SSD MobileNet 0.40 0.89 0.20 0.05 0.68 0.02 0.71 0.85
    Precision of SSD MobileNet 0.73 0.64 0.99 0.95 0.68 0.99 0.85 0.66
    Accuracy of SSD MobileNet 0.81 0.77 0.92 0.94 0.83 0.95 0.95 0.81
    Detection and classification results for each class

    View Slide

  19. Reporting by MCR & municipality patrol using RDD
    (September 2017)
    Incomplete
    Finished
    Observation
    Finding road damage
    Patrol route

    View Slide

  20. • patrol cars do not go in micro area units
    • Expects posts from citizens, especially where they are off the patrol's regular route 20
    Incomplete
    Finished
    Observation
    Finding road damage
    Patrol route

    View Slide

  21. ③ The results of Road Damage Detector
    • Maeda, H., Sekimoto, Y. and Seto, T.: Lightweight Road Manager: Smartphone-based
    Automatic Determination of Road Damage Status by Deep Neural Network,
    Proceedings of the 5th ACM SIGSPATIAL International Workshop on Mobile
    Geographic Information Systems (MobiGIS '16), pp.37-45, 2016.10.31
    https://doi.org/10.1145/3004725.3004729
    • Maeda, H., Sekimoto, Y., Seto, T., Kashiyama, T. and Omata, H.: Road Damage
    Detection and Classification Using Deep Neural Networks with Smartphone Images,
    Computer-Aided Civil and Infrastructure Engineering,
    https://doi.org/10.1111/mice.12387
    • SSD Inception V2&SSD MobileNet model and 10,000
    trained images are published CC-BY-SA4.0 license.
    https://github.com/sekilab/RoadDamageDetector
    • Organized by IEEE Big Data Conference2018 ”Road
    Damage Detection and Classification Challenge” 15
    countries, 52 teams participated.
    https://bdc2018.mycityreport.net/overview/
    21

    View Slide

  22. Connecting with researchers all over the world by
    open data !
    Published road damage data with 10,000 images (CC-BY-SA 4.0)
    https://github.com/sekilab/RoadDamageDetector)
    • IEEE Big Data Conference ”Road Damage
    Detection and Classification Challenge”
    • Participated with 52teams from 15 countries
    • The winning team is an ensemble of
    multiple deep learning methods, and the
    winning team is a Faster R-CNN base
    • https://bdc2018.mycityreport.net/
    ※プライバシー保護のため、⼈の顔、
    ⾞のナンバープレートにモザイクをかけています。
    Announcement of the result
    Gold Prize
    Silver Prize
    Bronze Prize
    Special Prize
    IMSC@USC (F1 = 0.64)
    Abdullah Alfarrarjeh, Dweep Trivedi,
    Seon Ho Kim, and Cyrus Shahabi
    CMBC_CHALLENGERS (F1 = 0.68)
    Yanbo J. Wang, Ming Ding, Shichao Kan,
    Shifeng Zhang, Chenyue Lu, and Qi Hong
    DSSC@BUPT (F1 = 0.65)
    Wenzhe Wang, Bin Wu,
    Sixiong Yang, Zhixiang Wang
    Bodo Rosenhahn's group (F1 = 0.63)
    Florian Kluger, Christoph Reinders,
    Kevin Raetz, Philipp Schelske, Bastian Wandt,
    Hanno Ackermann, and Bodo Rosenhahn

    View Slide

  23. ④ Development of Road Damage Detector Dashboard:
    Real-time Display of Patrol results using RDD App.
    23

    View Slide

  24. ④ Development of Road Damage Detector Dashboard: Road
    Evaluation and Cost Estimation by Road Data of the City
    24

    View Slide

  25. https://www.mycityreport.jp/
    ①MCR Smartphone App.
    ②MCR Management System ④ Road Damage Detector Dashboard
    ③ Road Damage Detector
    25
    Application for citizens
    Collaboration
    Application for
    road managers
    Conclusions

    View Slide

  26. Future Works
    • Comparing citizen reporting by some cities
    – Quantitative and qualitative
    – Analysis of long-term activities in Chiba-repo
    • Designing for Motivation of Citizen Reporting
    – Wellbeing, positive computing…
    – Peters D, Calvo RA and Ryan RM (2018) Designing for Motivation,
    Engagement and Wellbeing in Digital Experience. Front. Psychol. 9:797.
    doi: 10.3389/fpsyg.2018.00797
    26
    Peters et al. Designing for Motivation and Wellbeing
    FIGURE 2 | Taxonomy of Human Motivation; (A) Type of regulation, (B) Type of motivation, and (C) Examples translated to the user experience context (Adapted from
    Ryan and Deci, 2000a).

    View Slide

  27. Launched My City Report Consortium!
    Thank you !
    https://mycityreport.jp
    [email protected]
    27

    View Slide