Identification of construction and demolition waste based on change detection and deep learning

ABSTRACT

With the gradual adjustment of urban expansion and discontinuation of non-essential functions in Beijing, many decades-old buildings have been demolished. Thus, construction and demolition waste (CDW) has become the focus of urban and dust pollution management. However, CDW piles are volatile and present irregular boundaries. Therefore, it is essential to map CDW regions in a timely and accurate manner to achieve urban development while protecting the environment. To address this issue, we proposed a method of CDW identification based on change detection and deep learning. First, ZY-3 multispectral images from 2016 and 2019 and their difference images were used for initial sample preparation. We expanded the samples using the post-classification comparison method of change detection, resulting in a 25.4% increase in the valid sample set. The expanded samples were then used as an input to the DeepLabV3+ for training. Thereafter, combined with the change information from the digital elevation model, specific forms, such as demolition remains, landfill CDW, and large-scale dump, were extracted using spatial analysis methods. The overall accuracy of CDW recognition was 91.67%, with a Kappa coefficient of 0.8642. In addition, we calculated the accuracy indices using only the initial samples, obtaining a mean Intersection-over-Union value that was 0.086 lower than that obtained using the expanded sample set. Similar results were obtained in PSPNet and UNet. This suggests that change detection is useful in improving the accuracy of the deep learning models. This study is the first to identify three existing forms of CDW and can effectively address the misclassification between CDW and bare land to identify CDW efficiently.

KEYWORDS:

• Construction and demolition waste
• change detection
• deep learning
• ZY-3 multispectral and stereo image
• spatial analysis

Acknowledgements

The authors would like to thank the China Center for Resources Satellite Data and Application for providing the ZY-3 images. Additionally, the authors would like to specially thank the anonymous reviewers and editors for their very useful comments and suggestions to help improve the quality of this paper.

Disclosure statement

We declare that we have no financial and personal relationships with other people or organisations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript.

Data availability statement

The data used to support the findings of this study are available from the corresponding author upon request.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under the grant [42071422]; the National Key Research and Development Program of China under the grant [2018YFC0706004]; and the Beijing Natural Science Foundation under the grant [8202024].