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 !
• 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
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
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
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
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
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
! 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
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).