A Scoping Review of Deep Learning for Urban Visual Pollution and Proposal of a Real-Time Monitoring Framework with a Visual Pollution Index

2026-02-10

AI Summary

This study addresses the lack of a unified, automated framework for detecting, evaluating, and standardizing urban visual pollution—a gap exacerbated by fragmented existing research and limited real-time applicability. Following the PRISMA-ScR guidelines, the authors systematically review 26 core studies to assess the performance of mainstream object detection models, including YOLO, Faster R-CNN, and EfficientDet, in identifying visual pollutants. The work proposes a novel integrated framework that combines a standardized pollutant classification system, a cross-city benchmark dataset, a generalizable deep learning model, and a visual pollution index. This framework enables real-time monitoring and quantitative assessment of urban visual pollution, thereby establishing a technical and methodological foundation for advancing sustainable urban aesthetics and enhancing residents’ well-being.

Technology Category

• Computer Vision: Object Detection & Categorization
• Search and Optimization: Evaluation and Analysis
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Sustainability and carbon-aware systems for Web, mobile, and WoT
• Web Mining and Content Analysis: Web data visualization

Abstract

Urban Visual Pollution (UVP) has emerged as a critical concern, yet research on automatic detection and application remains fragmented. This scoping review maps the existing deep learning-based approaches for detecting, classifying, and designing a comprehensive application framework for visual pollution management. Following the PRISMA-ScR guidelines, seven academic databases (Scopus, Web of Science, IEEE Xplore, ACM DL, ScienceDirect, SpringerNatureLink, and Wiley) were systematically searched and reviewed, and 26 articles were found. Most research focuses on specific pollutant categories and employs variations of YOLO, Faster R-CNN, and EfficientDet architectures. Although several datasets exist, they are limited to specific areas and lack standardized taxonomies. Few studies integrate detection into real-time application systems, yet they tend to be geographically skewed. We proposed a framework for monitoring visual pollution that integrates a visual pollution index to assess the severity of visual pollution for a certain area. This review highlights the need for a unified UVP management system that incorporates pollutant taxonomy, a cross-city benchmark dataset, a generalized deep learning model, and an assessment index that supports sustainable urban aesthetics and enhances the well-being of urban dwellers.

Problem

• Urban Visual Pollution
• Deep Learning
• Real-Time Monitoring
• Visual Pollution Index
• Scoping Review

Innovation

• Visual Pollution Index
• real-time monitoring framework
• deep learning for urban visual pollution
• unified UVP management system
• cross-city benchmark dataset