# Machine-learned Regularization and Polygonization of Building Segmentation Masks

arXiv: https://arxiv.org/abs/2007.12587 · HTML: https://mlsanity.com/paper/2007.12587/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2020.

## Built on by (most cited first, 22 of 22)

- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions](https://mlsanity.com/paper/2404.04823/) (2024)
- [BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction](https://mlsanity.com/paper/2211.03373/) (2022)
- [A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery](https://mlsanity.com/paper/2104.01263/) (2021)
- [Machine-learned 3D Building Vectorization from Satellite Imagery](https://mlsanity.com/paper/2104.06485/) (2021)
- [Pix2Poly: A Sequence Prediction Method for End-to-end Polygonal Building Footprint Extraction from Remote Sensing Imagery](https://mlsanity.com/paper/2412.07899/) (2024)
- [Accurate Polygonal Mapping of Buildings in Satellite Imagery](https://mlsanity.com/paper/2208.00609/) (2022)
- [HiT: Building Mapping with Hierarchical Transformers](https://mlsanity.com/paper/2309.09643/) (2023)
- [PolyR-CNN: R-CNN for end-to-end polygonal building outline extraction](https://mlsanity.com/paper/2407.14912/) (2024)
- [P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images](https://mlsanity.com/paper/2406.02930/) (2024)
- [Data Leakage Detection and De-duplication in Large Scale Geospatial Image Datasets](https://mlsanity.com/paper/2304.02296/) (2023)
- [PolyBuilding: Polygon Transformer for End-to-End Building Extraction](https://mlsanity.com/paper/2211.01589/) (2022)
- [Building Footprint Extraction in Dense Areas using Super Resolution and Frame Field Learning](https://mlsanity.com/paper/2309.01656/) (2023)
- [PolyFootNet: Extracting Polygonal Building Footprints in Off-Nadir Remote Sensing Images](https://mlsanity.com/paper/2408.08645/) (2024)
- [LDPoly: Latent Diffusion for Polygonal Road Outline Extraction in Large-Scale Topographic Mapping](https://mlsanity.com/paper/2504.20645/) (2025)
- [Uncertainty, Edge, and Reverse-Attention Guided Generative Adversarial Network for Automatic Building Detection in Remotely Sensed Images](https://mlsanity.com/paper/2112.05335/) (2021)
- [Transformers For Recognition In Overhead Imagery: A Reality Check](https://mlsanity.com/paper/2210.12599/) (2022)
- [BiSVP: Building Footprint Extraction via Bidirectional Serialized Vertex Prediction](https://mlsanity.com/paper/2303.00300/) (2023)
- [GeoFormer: A Multi-Polygon Segmentation Transformer](https://mlsanity.com/paper/2411.16616/) (2024)
- [Unit Region Encoding: A Unified and Compact Geometry-aware Representation for Floorplan Applications](https://mlsanity.com/paper/2501.11097/) (2025)
- [Primitive Graph Learning for Unified Vector Mapping](https://mlsanity.com/paper/2206.13963/) (2022)
- [PolyTopoBench: A Benchmark for Complex Vector Polygon Generation from Remote Sensing Imagery](https://mlsanity.com/paper/2609.32856/) (2026)

## Abstract (arXiv)

We propose a machine learning based approach for automatic regularization and polygonization of building segmentation masks. Taking an image as input, we first predict building segmentation maps exploiting generic fully convolutional network (FCN). A generative adversarial network (GAN) is then involved to perform a regularization of building boundaries to make them more realistic, i.e., having more rectilinear outlines which construct right angles if required. This is achieved through the interplay between the discriminator which gives a probability of input image being true and generator that learns from discriminator's response to create more realistic images. Finally, we train the backbone convolutional neural network (CNN) which is adapted to predict sparse outcomes corresponding to building corners out of regularized building segmentation results. Experiments on three building segmentation datasets demonstrate that the proposed method is not only capable of obtaining accurate results, but also of producing visually pleasing building outlines parameterized as polygons.