标题： 基于吸引力场图的Transformer建筑边界重建 显示英文标题

标题： Transformer Based Building Boundary Reconstruction using Attraction Field Maps

摘要： 近年来，围绕地球运行的远程卫星数量显著增加，它们传输大量高分辨率视觉数据，以支持民事、公共和军事领域的各种应用。 在这些应用中，建筑物环境的空间地图生成和更新变得至关重要，因为卫星提供了广泛覆盖和详细图像。 然而，从卫星图像重建空间地图是一个复杂的计算机视觉任务，需要创建高级对象表示，如基本形状，以准确捕捉建筑物环境。 尽管过去十年在使用视觉数据进行对象检测和表示方面取得了显著进展，但基于基本形状的对象表示仍然是计算机视觉中的一个持续挑战。 因此，高质量的空间地图通常依赖于劳动密集和手动的过程。 本文介绍了一种新颖的深度学习方法，利用图卷积网络（GCNs）来解决建筑物轮廓重建中的这些挑战。 所提出的方法通过将几何规律纳入建筑物边界、整合多尺度和多分辨率特征以及将吸引力场图嵌入网络来提高性能。 这些创新为从单个卫星图像自动提取建筑物轮廓提供了可扩展且精确的解决方案，为城市规划、灾害管理和大规模空间分析中的有影响力的应用铺平了道路。 我们的模型，Decoupled-PolyGCN，在AP上比现有方法提高了6%，在AR上提高了10%，证明了其在多样和具有挑战性的场景中提供准确和规范化建筑物轮廓的能力。 显示英文摘要

摘要： In recent years, the number of remote satellites orbiting the Earth has grown significantly, streaming vast amounts of high-resolution visual data to support diverse applications across civil, public, and military domains. Among these applications, the generation and updating of spatial maps of the built environment have become critical due to the extensive coverage and detailed imagery provided by satellites. However, reconstructing spatial maps from satellite imagery is a complex computer vision task, requiring the creation of high-level object representations, such as primitives, to accurately capture the built environment. While the past decade has witnessed remarkable advancements in object detection and representation using visual data, primitives-based object representation remains a persistent challenge in computer vision. Consequently, high-quality spatial maps often rely on labor-intensive and manual processes. This paper introduces a novel deep learning methodology leveraging Graph Convolutional Networks (GCNs) to address these challenges in building footprint reconstruction. The proposed approach enhances performance by incorporating geometric regularity into building boundaries, integrating multi-scale and multi-resolution features, and embedding Attraction Field Maps into the network. These innovations provide a scalable and precise solution for automated building footprint extraction from a single satellite image, paving the way for impactful applications in urban planning, disaster management, and large-scale spatial analysis. Our model, Decoupled-PolyGCN, outperforms existing methods by 6% in AP and 10% in AR, demonstrating its ability to deliver accurate and regularized building footprints across diverse and challenging scenarios.