Building element recognition with MTL-AINet considering view perspectives

Abstract

Abstract The reconstruction and analysis of building models are crucial for the construction of smart cities. A refined building model can provide a reliable data support for data analysis and intelligent management of smart cities. The colors, textures, and geometric forms of building elements, such as building outlines, doors, windows, roof skylights, roof ridges, and advertisements, are diverse; therefore, it is challenging to accurately identify the various details of buildings. This article proposes the Multi-Task Learning AINet method that considers features such as color, texture, direction, and roll angle for building element recognition. The AINet is used as the basis function; the semantic projection map of color and texture, and direction and roll angle is used for multi-task learning, and the complex building facade is divided into similar semantic patches. Thereafter, the multi-semantic features are combined using hierarchical clustering with a region adjacency graph and the nearest neighbor graph to achieve an accurate recognition of building elements. The experimental results show that the proposed method has a higher accuracy for building detailed edges and can accurately extract detailed elements.