Smart technologies for urban cleanliness and waste management: an overview.

Abstract

Urbanisation and population growth have significantly increased waste generation, intensifying the need for efficient urban cleaning and waste management. Despite technological progress in other sectors, waste collection and street cleaning practices remain largely reactive, resource-intensive, and insufficiently supported by live information. In this context, smart and data-driven technologies offer new opportunities to improve operational efficiency, environmental performance, and urban quality of life. This study presents a structured overview of emerging digital technologies applied to municipal waste management, with a particular focus on street cleanliness assessment and Artificial Intelligence-based monitoring. The paper examines key technological domains, including Internet of Things systems, street-level imagery, mobile mapping, and machine learning methods for waste detection, classification, and decision support. Beyond summarising current solutions, the analysis identifies critical limitations in the field, notably the lack of standardised urban cleanliness indicators, limited availability of representative datasets for real-world conditions, and insufficient integration between objective measurements and citizen perception. Building on these insights, the paper proposes an integrated conceptual framework to address these gaps. The suggested methodology combines unmanned aerial vehicle-based image acquisition, computer vision techniques, and citizen perception surveys to support the development of a digitalised urban cleanliness index. This integrative approach enables scalable, data-driven, and citizen-informed urban cleanliness assessment, supporting more transparent and efficient decision-making in municipal waste management.