Urban Visual Pollution Dataset

Visual Pollution Types

The dataset covers a wide range of visual pollution categories, reflecting the diverse challenges faced by urban environments. These include:

• Graffiti: Unauthorized art or vandalism on public or private property.
• Faded Signage: Deteriorating signs that contribute to a neglected appearance.
• Potholes: Surface depressions in roadways that can cause vehicle damage and accidents.
• Garbage: Litter and improperly disposed waste in public areas.
• Construction Road: Temporary or abandoned construction sites that disrupt the urban landscape.
• Broken Signage: Damaged signs that may pose safety hazards and detract from the urban environment.
• Bad Streetlight: Faulty or insufficient street lighting that affects visibility and safety.
• Bad Billboard: Deteriorated or poorly maintained billboards that contribute to visual clutter.
• Sand on Road: Accumulations of sand or debris that can obscure road markings and pose driving hazards.
• Cluttered Sidewalk: Overcrowded pedestrian pathways with obstacles such as street vendors, debris, or parked vehicles.
• Unkept Facade: Building exteriors that are poorly maintained, contributing to a dilapidated urban appearance.

Dataset Composition

This dataset is composed of raw sensor camera inputs collected by a fleet of vehicles operating within a restricted geographic area in the Kingdom of Saudi Arabia (KSA). The imagery captures a wide array of urban scenes under different lighting and weather conditions, providing a robust dataset for training and testing machine learning models.

Applications and Use Cases

• Automated Visual Pollution Detection: Training AI models to automatically identify and categorize visual pollutants in urban environments.
• Urban Environmental Management: Developing tools to assess and mitigate visual pollution, leading to better urban planning and policy-making.
• Public Awareness and Engagement: Creating platforms to raise awareness about visual pollution and encourage community-driven efforts to improve urban aesthetics.
• Safety and Maintenance: Enhancing urban safety by identifying and addressing hazards like potholes, broken signage, and bad street lighting.

Potential Impact

The Urban Visual Pollution Dataset is poised to play a crucial role in shaping the future of urban environmental management. By enabling the development of sophisticated tools for detecting and evaluating visual pollution, this dataset supports efforts to create cleaner, safer, and more aesthetically pleasing urban spaces. The introduction of a visual pollution index could become a standard metric in urban planning, guiding interventions and policies to improve the quality of life in cities worldwide.

Future Directions

Future research could expand this dataset to include more geographic areas, different urban environments, and additional types of visual pollutants. There is also potential for integrating this dataset with other environmental data, such as air and noise pollution, to develop comprehensive urban health indices.

Conclusion

The Urban Visual Pollution Dataset is a critical resource for advancing the field of urban environmental management. By providing high-quality, diverse data, it empowers researchers and practitioners to address the growing challenge of visual pollution in cities, ultimately contributing to the development of more livable urban environments.