Urban Visual Pollution Detection in Public Realm Using Artificial Intelligence: A Case Study of Riyadh Area, Saudi Arabia

Visual pollution is a significant obstacle in the modern era, where the world is advancing towards increasingly diverse inventions. These inventions require a suitable environment to achieve accurate outcomes. Artificial intelligence has already permeated all fields and interests of life; similarly, visual pollution also needs to be addressed properly. Visual pollution often creates obstacles in performing various tasks. To mitigate these issues, an artificial intelligence-based model will play a vital role. This work deals with detecting visual pollution using an artificial intelligence-based algorithm to apply practical solutions that enhance urban public scenery. In the first step, a dataset is chosen from an authorized organization; specifically, the data is sourced from Mendeley, named the Saudi Arabia Public Roads Visual Pollution Dataset 2023. The second step involves data scaling and background removal from training images to facilitate learning in AI models. In the third step, the dataset is processed using Random Forest and support vector machine algorithms to visualize the model’s accuracy results. The support vector machine demonstrates better performance compared to the Random Forest.