GAST: Geometry-Aware Structure Transformer

We present GAST, a novel model for realistic building delineation, trained using noisy, imperfect ortho imagery and designed for real-life applications. While most popular methods today rely on some form of semantic segmentation, the core interest is not the building's interior points but rather the sequence of points surrounding the outer hull, i.e., the most sparse set of points encapsulating the geometry of the building. Our method works end-to-end, removing the need for post-processing, while demonstrating generalization across large geographical differences. We compare our method to state-of-the-art, complementary works and demonstrate that our model outperforms the baselines in a variety of circumstances and across all metrics relating to polygon fidelity. We release our dataset and model checkpoints at www.huggingface.co/datasets/pihalf/ERBD