Network model to optimize the process of green environmental features and urban building recognition based on lightweight image search system.

To promote the development of a green environment, it is necessary to build a friendly environment for green travel and increase the use of green resources. Methods based on remote sensing image processing and feature analysis have become one of the main ways to obtain ground information, and have been widely used and promoted. Therefore, ground targets the accuracy of object recognition and features have been significantly improved. In the lightweight image of the city, the terrain occupies almost 80% of the buildings and roads. Urban buildings play an important role in supporting the operation, management and planning of the city. An important technology for the construction of digital towns in the future is the city. Building identification, therefore, the research of building identification is more important in the development of the city. With the widespread application of artificial intelligence deep learning and the popularization of smart technologies, the digital images generated by the Internet and mobile smart terminals have exponentially increased. Image data provides users with a comfortable experience and convenient services, but it also brings many challenges. How to filter out the photos we are interested in from a large amount of image data and find the content we need is a data difficulty. In computer vision research, image search systems can solve the problem of searching for the same content in many digital images. This paper applies deep learning and lightweight images to search systems to promote the development of urban building recognition. [ABSTRACT FROM AUTHOR]