Remote sensing identification of green plastic cover in urban built-up areas

Abstract

Urban renewal can transform areas that are not adapted to modern urban life, allowing them to redevelop and flourish; however, the renewal process generates many new construction sites, producing environmentally harmful construction dust. The widespread use of urban green plastic cover (GPC) at construction sites and the development of high-resolution satellites have made it possible to extract the spatial distribution of construction sites and provide a basis for environmental protection authorities to protect against dust sources. Existing GPC extraction methods based on remote sensing images are either difficult to obtain the exact boundary of GPC or cannot provide corresponding algorithms according to different application scenarios. In order to determine the distribution of green plastic cover in the built-up area, this paper selects a variety of typical machine learning algorithms to classify the land cover of the test area image and selects K-nearest neighbor as the best machine learning algorithm through accuracy evaluation. Then multiple deep learning methods were used and the top networks with high overall scores were selected by comparing various aspects. Then these networks were used to predict the GPC of the test area image, and the accuracy evaluation results showed that the segmentation accuracy of deep learning was much higher than that of machine learning methods, but it took more time to predict. Therefore, combining different application scenarios, this paper gives the corresponding suggested methods for GPC extraction.

Keywords: Construction dust; Deep learning; Green plastic cover; Machine learning; Urban renewal.