Crowdsourced Civic Issue Reporting System Using AI

This research presents an AI-driven crowdsourced civic issue reporting system designed to enhance the efficiency, transparency, and responsiveness of urban governance. With the rapid growth of cities and increasing pressure on public infrastructure, traditional complaint management systems have proven inadequate due to their manual nature, delayed responses, and lack of intelligent decision-making capabilities. The proposed system addresses these challenges by integrating Artificial Intelligence with citizen participation to create a smart and scalable solution. The system leverages advanced technologies such as Computer Vision and Natural Language Processing (NLP) to analyze multi-modal data, including images, text descriptions, and geolocation information. This enables accurate detection, classification, and prioritization of civic issues in real time. The inclusion of duplicate detection mechanisms further improves efficiency by reducing redundant complaints, while the prioritization module ensures that critical issues are addressed promptly. Experimental results demonstrate that the proposed system significantly outperforms traditional and basic digital platforms in terms of classification accuracy, response time, and overall efficiency. The ability to process large volumes of data quickly and accurately makes the system highly suitable for modern smart city environments. Additionally, the platform promotes citizen engagement and improves transparency by allowing users to actively participate in governance processes. Despite its advantages, the system has certain limitations, including dependency on user participation, variability in input data quality, and computational challenges associated with large-scale deployment. Privacy and data security also remain important considerations for future implementation. In conclusion, the proposed AI-driven framework provides a robust, scalable, and intelligent solution for managing civic issues. Future enhancements, such as integration with IoT devices, advanced AI models, and government systems, can further improve its effectiveness and contribute to the development of smarter and more responsive urban environments.

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