Semantic Segmentation for Floor plans of Dwelling units to extract Room Attributes

Abstract

Designers for condominiums refer existing floor plans to create new floor plans that current trends and potential customer needs are taken into account. In this study, we applied semantic segmentation to the floor plans to extract the attributes of rooms and their boundaries, in order to create a database of the floor plans useful to the design work. We investigated the effects of pre-processing such as binarization and enlargement, and the differences between DeepLabv3 and HRNetOCR on the accuracy to enhance the segmentation. As a result, we confirmed that the practical accuracy can be obtained by considering both overall and local features in the inference of floor plans.