Drone-Based Remote Sensing for Monitoring Illegal Waste Sites: A Scalable Framework for Urban Environmental Management in Zambia

Abstract

Illegal waste dumping presents a persistent challenge in developing nations, posing severe environmental, social, and public health risks. Traditional inspection and monitoring systems remain inefficient, resource-intensive, and difficult to scale in fast-growing urban environments. This paper presents a novel drone-based remote sensing framework designed to detect, classify, and map illegal waste sites in real time using high-resolution imagery and artificial intelligence. The proposed system integrates object detection models with Geographic Information Systems (GIS) for spatial analytics and visualization, supported by a severity threshold algorithm that prioritizes high-risk sites based on image confidence and area metrics. Field deployment in Lusaka demonstrated an overall detection accuracy of 87%, with a mean precision of 0.89 and recall of 0.85, validating the system’s technical feasibility and robustness. Additionally, a public-facing reporting interface and an administrative dashboard were developed to strengthen community participation and enhance enforcement efficiency. The results show that this approach provides a scalable, cost-effective, and data-driven solution for environmental monitoring, with the potential for broad implementation across sub-Saharan Africa and other developing regions.