Irregular Facades: A Dataset for Semantic Segmentation of the Free Facade of Modern Buildings

Abstract

Semantic segmentation of building facades has enabled much intelligent support for architectural research and practice in the last decade. Faced with the free facade of modern buildings, however, the accuracy of segmentation decreased significantly, partly due to its low regularity of composition. The freely organized facade composition is likely to weaken the features of different elements, thus increasing the difficulty of segmentation. At present, the existing facade datasets for semantic segmentation tasks were mostly developed based on the classical facades, which were organized regularly. To train the pixel-level classifiers for the free facade segmentation, this study developed a finely annotated dataset named Irregular Facades (IRFs). The IRFs consist of 1057 high-quality facade images, mainly in the modernist style. In each image, the pixels were labeled into six classes, i.e., Background, Plant, Wall, Window, Door, and Fence. The multi-network cross-dataset control experiment demonstrated that the IRFs-trained classifiers segment the free facade of modern buildings more accurately than those trained with existing datasets. The formers show a significant advantage in terms of average WMIoU (0.722) and accuracy (0.837) over the latters (average WMIoU: 0.262–0.505; average accuracy: 0.364–0.662). In the future, the IRFs are also expected to be considered the baseline for the coming datasets of freely organized building facades.