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This article discusses the use of deep learning technology to accurately assess and quantify construction waste generation in urban areas. It also highlights the importance of high-quality public datasets for model training and validation. The study utilizes Google Earth and GF-2 images to construct a specific dataset of construction waste landfills in Beijing, China. The dataset contains pixel-level semantic segmentation labels and can serve as a valuable resource for both academic and industrial research.
