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Vision Statement

Overview​

The proposed system envisions a smarter urban platform where city operators and citizens collaboratively contribute to enhancing the overall quality of life in the city. This collaboration is essential for addressing urban challenges effectively and responsively.

Currently, incident reporting is often conducted manually through inefficient channels such as phone calls or emails. These fragmented methods lead to delayed responses and hinder the ability of municipal authorities to prioritize and resolve the most pressing and recurring urban issues. The absence of a direct and structured communication channel between citizens and public services further exacerbates these inefficiencies.

To address these limitations, the platform seeks to modernize the entire lifecycle of urban issue management—from initial reporting to resolution—by leveraging artificial intelligence, geospatial indexing, and scalable infrastructure. This vision aligns with the principles of digital transformation in public administration and contributes to broader smart city initiatives.

Challenges​

The project also aims to address several challenges associated with this type of application:

  • Avoiding duplicate reports: Grouping similar complaints and avoiding redundant submissions to improve information management.
  • Enhancing classification accuracy: Automating classification and descriptions for improved usability and reliability.
  • Integrating sensors and AI-based verification: Leveraging ATCLL infrastructure, including PIXKIT, to detect resolution of incidents.
  • Scalability: Designing an efficient architecture that can handle a large volume of reports and data.
  • Event data acquisition and tracking: Implementing a platform for incident reporting with precise location tracking.
  • Real-time notifications: Providing timely updates to users to enhance engagement and responsiveness.
  • Comprehensive documentation: Creating detailed documentation for future maintainability and platform evolution.

Differentiation​

The AI-Powered Platform for Smart City Issue Detection & Resolution differentiates itself by emphasizing:

  • AI-Based Classification and Verification: Utilizing LLMs for incident classification and description generation, reducing manual effort.
  • Geospatial Indexing: Employing the H3 framework for efficient geospatial indexing and clustering of similar reports, enhancing data management.
  • Citizens: Empowering citizens to actively participate in urban governance by submitting reports and receiving real-time updates.
  • Scalability: Designed to handle a large volume of reports and data, ensuring efficient performance even in high-demand scenarios.

This approach sets it apart from traditional reporting systems, which often rely on manual processing, delayed verification, and lack of automated classification.