Exploring The Capabilities of Single-Stage Instance Segmentation Models for Building Extraction

Building extraction from aerial and satellite imagery is a crucial task for applications such as urban planning, disaster management, and geospatial analysis. Despite recent advances in deep learning, the automatic extraction of buildings remains challenging due to the diversity of building characteristics and surrounding complexities. In this work, we explore the effectiveness of a single-stage instance segmentation model for building extraction as a potentially better alternative to two-stage models. Namely, we employ RTMDet, a state-of-the-art single-stage instance segmentation model, to investigate its performance in building extraction tasks. RTMDet offers flexibility between segmentation performance and inference speed, which we assess in terms of accuracy and efficiency. Furthermore, we examine the impact of data augmentation techniques, particularly affine transformations, and the impact of the amount of training data on improving model performance. Experimental results demonstrate a comprehensive comparison of several training configurations as well as a comparison between single-stage and two-stage instance segmentation approaches. Our source codes and the best-performing model parameters are available at https://github.com/tamtamz/bigdata2024-buildext.