Polygonizer: An auto-regressive building delineator

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Abstract

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.
Original languageEnglish
Title of host publicationProceedings of the ICLR 2023 Workshop on Machine Learning for Remote Sensing
Number of pages9
Publication date2023
Publication statusPublished - 2023
EventEleventh International Conference on Learning Representations - Kigali Convention Centre, Kigali , Rwanda
Duration: 1 May 20235 May 2023
Conference number: 11
https://iclr.cc/Conferences/2023

Conference

ConferenceEleventh International Conference on Learning Representations
Number11
LocationKigali Convention Centre
Country/TerritoryRwanda
CityKigali
Period01/05/202305/05/2023
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