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1715486 Vol 9 · Issue 9 Download Paper

AI-Assisted Waste Reporting and Community Cleanup Platform

Vasantha Kumar S M. Asan Nainar

Subject area: Science,Engineering and Technology  ·  Area of research: SmartCity Applications

DOI: 10.64388/IREV9I9-1715486

Abstract

The AI-Assisted Waste Reporting and Community Cleanup Platform, developed under the initiative "Madurai Smart Clean", is a full-stack cross-platform mobile application designed to bridge the gap between citizens, municipal authorities, volunt⁠eers, and administrators for efficient urban waste manag⁠ement. Built using React Nati⁠ve (Ex⁠po) on the fro⁠ntend an⁠d Python Flask on the backend with a MySQL relational databa⁠se, the system provides role-b⁠ased access control for four distinct user groups. Citize⁠ns can report geo-tagged wa⁠ste via a dedicated reporting screen, track the status of their submissions, and raise emergency san⁠itation requests. Municipal authorities revi⁠ew and resolve complaints through a dedicated dashboard and maintain a cleanu⁠p histo⁠ry for transparency. Volunteers participate in community-driven cleanup events through the Volunteer Hub, register vehicles for waste collection, and are rewarded through a gamified leaderboard system. Admini⁠strat⁠ors oversee the entire platform, manage⁠ es⁠calations, allocate emergency funds, and coordinate logistics. Additional m⁠odules includ⁠e a real-time waste hotspot map powered by React Native Maps, an Awareness Center for hygiene education, a donation gateway titled Help Madurai for⁠ community funding, and a dedicated eme⁠rgency fund management interface. The⁠ backend is structured through nine F⁠lask API blueprints coverin⁠g a⁠uthentication, complaints, authority review, administration, volunteer coordination, awareness, fund manag⁠ement, emergency routing, and location services. Experimental evaluat⁠ion across key functional modules demonstrates high accuracy and reliability, validating the platform as a scalable, community-driven solution for urban cleanliness management.

Keywords

Urban Waste Management, Crowdsourced Reporting, Geo-Tagged Volunteer Hub, Emergency Management, Leaderboard, Community Cleanup.

References

[1] P. Verma, A. Sharma, and R. Katkuri, "AI-Driven Urban Waste Detection and Automated Reporting Using Deep Learning on Mobile Platforms," IEEE Access, vol. 12, pp. 14320–14335, 2024.

[2] M. Chen, L. Zhang, and H. Wang, "Real-Time Garbage Classification Using CNNs for Smart City Waste Management," IEEE Trans. Ind. Informat., vol. 20, no. 1, pp. 401–412, 2024.

[3] S. Anand, T. Ravi, and P. Krishnamurthy, "Crowdsourced Geo-Tagged Civic Complaint Systems: Lessons from Indian Urban Bodies," Int. J. Smart City Appl., vol. 8, no. 2, pp. 88–102, 2023.

[4] R. Katkuri, A. Sharma, and P. Verma, "Volunteer Engagement in Smart City Initiatives: A Gamification Framework," IEEE Conf. Smart Systems and Emerging Technologies, pp. 210–218, 2023.

[5] B. W. Schuller, A. Mallol-Ragolta, and N. Cummins, "Multimodal Urban Environmental Monitoring: Sensor Fusion and AI Classification," IEEE Trans. Intell. Transp. Syst., vol. 24, no. 6, pp. 6100–6115, 2023.

[6] N. Siddiqui and F. Al-Turjman, "Role-Based Access Control in IoT-Enabled Smart Municipal Platforms," Future Gener. Comput. Syst., vol. 138, pp. 310–322, 2023.

[7] R. Sharma and D. Patel, "Integration Challenges in Municipal Civic Technology Platforms: A Systematic Review," IEEE Trans. Smart Cities, vol. 3, no. 4, pp. 1122–1135, 2022.

[8] S. Parashakthi and R. Savithri, "Mobile-Based Geo-tagged Complaint Management for Urban Local Bodies," Int. J. Comput. Appl., vol. 184, no. 32, pp. 1–8, 2022.

[9] T. Nguyen, V. Le, and P. Do, "Duplicate Report Detection in Crowdsourced Urban Issue Platforms Using Geospatial Hashing," J. Netw. Comput. Appl., vol. 198, p. 103295, 2022.

[10] A. Dhall, M. Kumar, and S. Rao, "Deep Learning for Visual Waste Severity Classification in Urban Environments," IEEE CVPR Workshops, pp. 3420–3428, 2021.

[11] R. Kumar, A. Singh, and P. Mehta, "Crowdsourced Civic Issue Reporting: Reducing Municipal Response Times via Mobile Geo-tagging," Int. J. Smart City Appl., vol. 5, no. 1, pp. 45–58, 2021.

[12] J. Park, S. Kim, and Y. Lee, "Gamification Strategies for Sustained Civic Participation in Mobile Public Service Applications," Comput. Human Behav., vol. 115, p. 106592, 2021.

[13] S. Deterding, D. Dixon, R. Khaled, and L. Nacke, "From Game Design Elements to Gamefulness: Defining Gamification," Proc. 15th Int. Academic MindTrek Conf., pp. 9–15, 2020.

[14] M. Haworth, P. Wheatley, and J. Bhatt, "RESTful API Design Patterns for Scalable Flask-Based Microservices in Smart City Architectures," Int. J. Web Serv. Res., vol. 17, no. 3, pp. 23–41, 2020.

How to cite this paper

Vasantha Kumar S, M. Asan Nainar "AI-Assisted Waste Reporting and Community Cleanup Platform" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 2357-2365 https://doi.org/10.64388/IREV9I9-1715486
Vasantha Kumar S, M. Asan Nainar "AI-Assisted Waste Reporting and Community Cleanup Platform" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026, doi: https://doi.org/10.64388/IREV9I9-1715486
Vasantha Kumar S, M. Asan Nainar (2026). AI-Assisted Waste Reporting and Community Cleanup Platform. Iconic Research And Engineering Journals, 9(9). doi: https://doi.org/10.64388/IREV9I9-1715486
Vasantha Kumar S, M. Asan Nainar "AI-Assisted Waste Reporting and Community Cleanup Platform" Iconic Research And Engineering Journals, vol. 9, no. 9, Mar. 2026. Crossref, https://doi.org/10.64388/IREV9I9-1715486
@article{1715486,
      author = {Vasantha Kumar S, M. Asan Nainar},
      title = {AI-Assisted Waste Reporting and Community Cleanup Platform},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {9},
      pages = {2357-2365},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715486.pdf},
      abstract = {The AI-Assisted Waste Reporting and Community Cleanup Platform, developed under the initiative "Madurai Smart Clean", is a full-stack cross-platform mobile application designed to bridge the gap between citizens, municipal authorities, volunt⁠eers, and administrators for efficient urban waste manag⁠ement. Built using React Nati⁠ve (Ex⁠po) on the fro⁠ntend an⁠d Python Flask on the backend with a MySQL relational databa⁠se, the system provides role-b⁠ased access control for four distinct user groups. Citize⁠ns can report geo-tagged wa⁠ste via a dedicated reporting screen, track the status of their submissions, and raise emergency san⁠itation requests. Municipal authorities revi⁠ew and resolve complaints through a dedicated dashboard and maintain a cleanu⁠p histo⁠ry for transparency. Volunteers participate in community-driven cleanup events through the Volunteer Hub, register vehicles for waste collection, and are rewarded through a gamified leaderboard system. Admini⁠strat⁠ors oversee the entire platform, manage⁠ es⁠calations, allocate emergency funds, and coordinate logistics. Additional m⁠odules includ⁠e a real-time waste hotspot map powered by React Native Maps, an Awareness Center for hygiene education, a donation gateway titled Help Madurai for⁠ community funding, and a dedicated eme⁠rgency fund management interface. The⁠ backend is structured through nine F⁠lask API blueprints coverin⁠g a⁠uthentication, complaints, authority review, administration, volunteer coordination, awareness, fund manag⁠ement, emergency routing, and location services. Experimental evaluat⁠ion across key functional modules demonstrates high accuracy and reliability, validating the platform as a scalable, community-driven solution for urban cleanliness management.},
      keywords = {Urban Waste Management, Crowdsourced Reporting, Geo-Tagged Volunteer Hub, Emergency Management, Leaderboard, Community Cleanup.},
      month = {March},
      doi = {https://doi.org/10.64388/IREV9I9-1715486}
  }