Skip to yearly menu bar Skip to main content

Workshop: ICLR 2023 Workshop on Machine Learning for Remote Sensing

Polygonizer: An auto-regressive building delineator

Maxim Khomiakov · Michael Andersen · Jes Frellsen


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.

Chat is not available.