Urban Residential Wind Field Visualization and Wind Speed Recognition System Based on Image Fusion with Health Risk Early Warning

With the acceleration of urbanization, improving the wind environment in urban residential areas has become a crucial issue for enhancing residents' quality of life and health safety. Traditional wind field monitoring methods have significant limitations and fail to meet the demand for large-scale, high-resolution, real-time wind field monitoring. Therefore, developing new technologies to improve the accuracy of wind field visualization and to provide scientific support for urban planning has become an urgent research need. This study proposes an urban residential wind field visualization and wind speed recognition system based on image fusion. By combining infrared hyperspectral images with their extracted feature values, a technical framework for wind field visualization and wind speed recognition is developed, leading to the implementation of a health risk early warning mechanism. Existing research primarily relies on physical models, sensor networks, and remote sensing technologies to study wind field distribution and wind speed variations; however, these methods often suffer from limitations in spatial coverage, data fusion capabilities, and computational efficiency. By introducing an innovative image fusion technique, this study overcomes the limitations of traditional approaches, offering a more precise visualization solution for urban residential wind fields. Furthermore, by leveraging feature values from infrared hyperspectral images for wind speed recognition, the system enables proactive health risk warnings. This method provides effective technical support for urban planning, environmental protection, and public health management, and holds significant theoretical and practical value.