PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction from High-Resolution Remote Sensing Images

• URL: http://arxiv.org/abs/2606.08920v1
• Date: Mon, 08 Jun 2026 01:59:36 GMT
• Title: PolyBuild: An End-to-End Method for Polygonal Building Contour Extraction from High-Resolution Remote Sensing Images
• Abstract summary: We propose an end-to-end method named PolyBuild, which can directly extract building polygons from high-resolution remote sensing images.<n>The proposed method leverages two primary modules: an Initial Contour Generation Module (ICGM) and a Contour Optimization Module (COM)<n>The results demonstrate that PolyBuild significantly outperforms state-of-the-art methods, including mask-based and contour-based approaches.
• Score: 2.216908642110801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Extracting building polygon contours from high-resolution remote sensing images is a fundamental task for various mapping applications. However, the presence of varying imaging conditions and complex building structures, makes automatic contour extraction extremely challenging. Mainstream approaches for building extraction often rely on pixel-level segmentation followed by multiple post-processing steps to produce building contour, which can be computationally intensive and prone to errors. In this paper, we propose an end-to-end method named PolyBuild, which can directly extract building vector polygons from high-resolution remote sensing images without the need for any post-processing operations. The proposed method leverages two primary modules: an Initial Contour Generation Module (ICGM) and a Contour Optimization Module (COM). The ICGM is designed to generate an initial building contour by utilizing concatenated sub-region center features for each building instance. It performs simultaneous object detection and initial contour extraction by generating bounding boxes and using the center features of four sub-regions to represent each building. The Contour Optimization Module (COM) further refines the generated building contours by iteratively integrating Convolutional Neural Network (CNN) features and contour positional information in a Transformer-based decoder. The hybrid CNN-Transformer architecture effectively captures both local and global spatial relationships within the building contour, ensuring high-quality boundary delineation. Extensive experiments are conducted on three building datasets to evaluate the performance of PolyBuild. The results demonstrate that PolyBuild significantly outperforms state-of-the-art methods, including mask-based and contour-based approaches.

Related papers

• Towards Realistic and Consistent Orbital Video Generation via 3D Foundation Priors [61.34273238077091]
  We present a novel method for generating orbital videos from a single image of an object.<n>Our method achieves superior visual quality, shape realism and multi-view consistency compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-04-14T05:35:46Z)
• Light-SQ: Structure-aware Shape Abstraction with Superquadrics for Generated Meshes [60.92139345612904]
  We present Light-SQ, a novel superquadric-based optimization framework.<n>We propose a block-regrow-fill strategy guided by structure-aware volumetric decomposition.<n>Experiments demonstrate that Light-SQ enables efficient, high-fidelity, and editable shape abstraction with superquadrics.
  arXiv  Detail & Related papers  (2025-09-29T16:18:32Z)
• Global Collinearity-aware Polygonizer for Polygonal Building Mapping in Remote Sensing [18.151134198549574]
  This paper addresses the challenge of mapping polygonal buildings from remote sensing images.<n>It introduces a novel algorithm, the Global Collinearity-aware Polygonizer (GCP)
  arXiv  Detail & Related papers  (2025-05-02T16:49:07Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• P2PFormer: A Primitive-to-polygon Method for Regular Building Contour Extraction from Remote Sensing Images [5.589842901102337]
  Existing methods struggle with irregular contours, rounded corners, and redundancy points.<n>We introduce a novel, streamlined pipeline that generates regular building contours without post-processing.<n>P2PFormer achieves new state-of-the-art performance on the WHU, CrowdAI, and WHU-Mix datasets.
  arXiv  Detail & Related papers  (2024-06-05T04:38:45Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• HiT: Building Mapping with Hierarchical Transformers [43.31497052507252]
  We propose a simple and novel building mapping method with Hierarchical Transformers, called HiT. HiT builds on a two-stage detection architecture by adding a polygon head parallel to classification and bounding box regression heads. Our method achieves a new state-of-the-art in terms of instance segmentation and polygonal metrics compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-09-18T10:24:25Z)
• BuildMapper: A Fully Learnable Framework for Vectorized Building Contour Extraction [3.862461804734488]
  We propose the first end-to-end learnable building contour extraction framework, named BuildMapper. BuildMapper can directly and efficiently delineate building polygons just as a human does. We show that BuildMapper can achieve a state-of-the-art performance, with a higher mask average precision (AP) and boundary AP than both segmentation-based and contour-based methods.
  arXiv  Detail & Related papers  (2022-11-07T08:58:35Z)
• PolyBuilding: Polygon Transformer for End-to-End Building Extraction [9.196604757138825]
  PolyBuilding predicts vector representation of buildings from remote sensing images. Model learns the relations among them and encodes context information from the image to predict the final set of building polygons. It also achieves a new state-of-the-art in terms of pixel-level coverage, instance-level precision and recall, and geometry-level properties.
  arXiv  Detail & Related papers  (2022-11-03T04:53:17Z)
• Generating Diverse Structure for Image Inpainting With Hierarchical VQ-VAE [74.29384873537587]
  We propose a two-stage model for diverse inpainting, where the first stage generates multiple coarse results each of which has a different structure, and the second stage refines each coarse result separately by augmenting texture. Experimental results on CelebA-HQ, Places2, and ImageNet datasets show that our method not only enhances the diversity of the inpainting solutions but also improves the visual quality of the generated multiple images.
  arXiv  Detail & Related papers  (2021-03-18T05:10:49Z)
• Quantization in Relative Gradient Angle Domain For Building Polygon Estimation [88.80146152060888]
  CNN approaches often generate imprecise building morphologies including noisy edges and round corners. We propose a module that uses prior knowledge of building corners to create angular and concise building polygons from CNN segmentation outputs. Experimental results demonstrate that our method refines CNN output from a rounded approximation to a more clear-cut angular shape of the building footprint.
  arXiv  Detail & Related papers  (2020-07-10T21:33:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.