classification; machine learning; deep learning; convolution neural networks; dataset; landfill waste; waste management; sustainability
This dataset includes three types: DEM, land cover data, and high-definition remote sensing images.
Among them, the DEM data has an accuracy of 30 meters and is used to calculate the slope value. The land cover data has an accuracy of 8.98 meters and is located in a field area in southern Taiwan, China, and is used to extract coarse-grained land object types. The high-definition remote sensing data is taken by drones with an accuracy of 0.03 meters and is used for unsupervised classification of land objects.
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The accurate classification of landfill waste diversion plays a critical role in efficient waste management practices. Traditional approaches, such as visual inspection, weighing and volume measurement, and manual sorting, have been widely used but suffer from subjectivity, scalability, and labour requirements. In contrast, machine learning approaches, particularly Convolutional Neural Networks (CNN), have emerged as powerful deep learning models for waste detection and classification.
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