Solid Waste Detection, Monitoring and Mapping in Remote Sensing Images: A Survey
The detection and characterization of illegal solid waste disposal sites are essential for environmental protection, particularly for mitigating pollution and health hazards. Improperly managed landfills contaminate soil and groundwater via rainwater infiltration, posing threats to both animals and humans. Traditional landfill identification approaches, such as on-site inspections, are time-consuming and expensive. Remote sensing is a cost-effective solution for the identification and monitoring of solid waste disposal sites that enables broad coverage and repeated acquisitions over time. Earth Observation (EO) satellites, equipped with an array of sensors and imaging capabilities, have been providing high-resolution data for several decades. Researchers proposed specialized techniques that leverage remote sensing imagery to perform a range of tasks such as waste site detection, dumping site monitoring, and assessment of suitable locations for new landfills. This review aims to provide a detailed illustration of the most relevant proposals for the detection and monitoring of solid waste sites by describing and comparing the approaches, the implemented techniques, and the employed data. Furthermore, since the data sources are of the utmost importance for developing an effective solid waste detection model, a comprehensive overview of the satellites and publicly available data sets is presented. Finally, this paper identifies the open issues in the state-of-the-art and discusses the relevant research directions for reducing the costs and improving the effectiveness of novel solid waste detection methods.
Code (0)
등록된 구현이 없습니다.
Tasks
Earth ObservationSimilar Papers 제목 키워드 기반
Open-access model for detecting openly dumped dispersed municipal solid waste from crowdsourced UAV imagery in Sub-Saharan Africa
Managing municipal solid waste in rapidly urbanizing Sub-Saharan Africa remains challenging due to dispersed informal dumping and limited high-resolution datasets for spatial monitoring. We present an open-access deep le…
AI-based Waste Mapping for Addressing Climate-Exacerbated Flood Risk
Urban flooding is a growing climate change-related hazard in rapidly expanding African cities, where inadequate waste management often blocks drainage systems and amplifies flood risks. This study introduces an AI-powere…
Synchronized Object Detection for Autonomous Sorting, Mapping, and Quantification of Materials in Circular Healthcare
The circular economy paradigm is gaining interest as a solution to reducing both material supply uncertainties and waste generation. One of the main challenges in realizing this paradigm is monitoring materials, since in…
object-detectionObject DetectionGarbage Vulnerable Point Monitoring using IoT and Computer Vision
This paper proposes a smart way to manage municipal solid waste by using the Internet of Things (IoT) and computer vision (CV) to monitor illegal waste dumping at garbage vulnerable points (GVPs) in urban areas. The syst…
Object DetectionIllegal Waste Detection in Remote Sensing Images: A Case Study
Environmental crime is the third largest criminal activity worldwide, with significant revenues coming from illegal management of solid waste. Thanks to the increasing availability and the decreasing cost of Very High Re…