AI-driven Environmental Data Analysis Case Study

This case study examines the application of AI-driven environmental data analysis by a provider assisting cities in identifying issues such as dirty traffic signs, road damage, and illegal waste dumping. The document outlines the challenges faced, including varying image quality due to weather conditions, insufficient GPS data, and complications from motion blur and image noise, which affected data accuracy. It also highlights the need for real-time data processing and scalability. The benefits of the implemented solutions include improved customer satisfaction through enhanced data quality, leveraging cloud scalability by migrating AI models to Google Cloud, and optimizing resource usage by efficiently processing and streaming data. The case study also discusses the Google Cloud solutions utilized, such as Cloud Run and Video Intelligence API, to address these challenges and improve operational efficiency.