Automated optimizing method for map labeling of buildings from remote sensing images

Label placement is a critical and challenging aspect of automated cartography. Traditional methods of automated map label placement mainly focus on vector maps or digital line graphics, and these methods often perform poorly when handling label placement on remote sensing images. This study proposes a method for building label placement that considers the visual characteristics of the image. This method first determines candidate labeling areas through superpixel segmentation and k-means clustering. Then a series of candidate labeling lines are derived using morphological computation, graph theory and other techniques. The labeling line that best fits the structural characteristics of the building is selected according to a predefined optimization strategy. Finally, the rotation angle of each character in the label is calculated according to the labeling line of the building, and the appropriate labeling style is determined based on the brightness characteristics of the labeling area. Compared with traditional methods, the proposed method thoroughly analyzes the visual features of building images, such as structure, color, texture, and brightness. During the label placement process, the proposed method comprehensively considers the texture complexity and morphological information of the label placement area, providing a more readable, natural, and aesthetically pleasing label placement solution.