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Published November 13, 2023 | Version 1.0.0
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A Construction Waste Landfill Dataset of Two Districts in Beijing, China from High Resolution Satellite Images

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CWLD constructs and forms a construction waste landfill dataset in Changping and Daxing districts of Beijing using Gaofen-2 remote sensing satellite as the data source. The dataset contains samples of the original image area and provides mask labeled images in the semantic segmentation domain.Each pixel inside a construction waste landfill is categorized in detail according to the image background area, the open space area, the engineering facility area and the waste dumping area. It contains 237,115,531 pixels of construction waste and 49,724,513 pixels of engineering facilities.

The dataset consists of three folders: Original Dataset, Construction Waste Landfill Dataset, and Deep Learning Datasets.

  • Original Dataset. Remote sensing images and labelled images are stored separately according to Changping and Daxing districts in the Original Dataset folder.
  • The Construction Waste Landfill Dataset folder contains the raw data enriched with data enhancement techniques.
  • Deep Learning Datasets. The input layer of the neural network model usually needs to have a fixed input size, so it is necessary to preprocess the data before training by adjusting the input data to 512×512px, which is divided into the training set and the validation set in accordance with 8:2.

Visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on


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Additional details


  • Torres, Rocio Nahime and Fraternali, Piero. "AerialWaste dataset for landfill discovery in aerial and satellite images." Scientific Data, vol. 10, no.1, p.63, 2023.
  • L. Zhou et al., "SWDet: Anchor-Based Object Detector for Solid Waste Detection in Aerial Images," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 306-320, 2023, doi: 10.1109/JSTARS.2022.3218958.