Research on Precise Control of Decoration Waste Based on GF-2 Remote Sensing Images and a BP Neural Network: A Case Study of Henan Province

Decoration waste, because of its complex composition and the presence of volatile toxic and hazardous substances, has always been a difficult point in the management of urban construction waste. And with the continuous expansion of the town scale, the volume of decoration waste is gradually expanding, which constitutes a major challenge to the sustainable development of the construction industry. In order to solve this difficult problem, this paper took Henan Province as an example, and realized the accurate control of decoration waste based on GF-2 remote sensing images and a BP neural network model. The results of GF-2 remote sensing image interpretation and analysis showed that the spatial distribution of construction waste in the study area was extracted through a combination of manual visual interpretation and machine learning recognition, and as of 2021, the construction waste pile occupied a large proportion of the land area, of which the proportion of decoration waste was about 10%. Based on the trained BP neural network, the goodness-of-fit result was R = 0.95463. Selecting the research data from 2010 to 2021, the error of the predicted annual generation of decoration waste in Henan Province compared with the actual value was less than 15%, which had a high prediction accuracy. Based on the arithmetic sum of the projected figures for each year from 2022 to 2030, it is estimated that by 2030, the cumulative volume of construction and renovation waste generated in Henan Province will reach 49,827,200 tons. Visualization of spatial and temporal distribution characteristics was realized through ArcGIS, and the high production area of decoration waste was distributed from the beginning to the end of the distribution of multi-points to show the characteristics of a concentrated large area distribution, centrally located in southwestern and southeastern Henan Province, with the key cities of Zhumadian City, Luoyang City, Zhoukou City, and Xinyang City, which had obvious regional characteristics. At the same time, as the provincial capital, Zhengzhou has long ranked first in the province in terms of absolute case numbers and is therefore also a key focus of control measures. Uncertainty analysis indicates that the 95% confidence interval for the long-term forecast values is approximately ±12%. It is recommended to use the upper limit of this interval for the redundancy design of the absorption facilities to enhance the robustness of the decision. This study provides a theoretical basis and technical support for the governmental supervision of decoration waste during the development of national urban agglomerations, effectively solves regional urban planning and construction management problems, and promotes the sustainable development of the construction industry.