Polygonizer: An auto-regressive building delineator
This addresses the need for efficient vector-based workflows in geospatial applications, though it appears incremental as it builds on prior methods with specific improvements.
The paper tackles the problem of vectorizing building outlines from images for geospatial planning by introducing an Image-to-Sequence model that directly infers shapes, eliminating the need for post-processing. It demonstrates superior performance, achieving the lowest maximum tangent angle error when using ground truth bounding boxes.
In geospatial planning, it is often essential to represent objects in a vectorized format, as this format easily translates to downstream tasks such as web development, graphics, or design. While these problems are frequently addressed using semantic segmentation, which requires additional post-processing to vectorize objects in a non-trivial way, we present an Image-to-Sequence model that allows for direct shape inference and is ready for vector-based workflows out of the box. We demonstrate the model's performance in various ways, including perturbations to the image input that correspond to variations or artifacts commonly encountered in remote sensing applications. Our model outperforms prior works when using ground truth bounding boxes (one object per image), achieving the lowest maximum tangent angle error.