From Above and Beyond: Decoding Urban Aesthetics with the Visual Pollution Index

Advait Gupta, Manan Padsala, Devesh Jani, Tanmay Bisen, Aastha Shayla, Susham Biswas

Abstract: Urban landscapes, emblematic of modernization and growth, are increasingly faced with the intricate challenge of visual pollution. This nuanced form of pollution, often overshadowed environmental discussions, profoundly influences the aesthetic harmony and mental well-being of urban inhabitants. In this research, we present an innovative methodology to detect visual pollution using drone-captured imagery. Our distinctive dataset captures a spectrum of visual pollutants, from graffiti, faded signage, and potholes to more complex issues like cluttered sidewalks and unkempt facades. Leveraging this dataset, we fine-tuned pre-trained object detection models, specifically YOLOv6, achieving remarkable accuracy in detecting these visual pollutants from images. Central to our study is the introduction of the Visual Pollution Index (VPI), a metric formulated through the multiplicative integration of the Counting Categories Ratio (CCR) and the Severity-Weighted Score (SWS). To provide a spatial representation of visual pollution levels, we further introduce heatmap visualizations. These heatmaps, overlaid on urban maps, offer a vivid depiction of pollution hotspots, enabling city planners and stakeholders to pinpoint areas of concern. Grounded in real-world perceptions, our approach offers a comprehensive lens to assess, visualize, and address visual pollution in urban environments.