Abstract
This paper addresses the challenge of mapping polygonal buildings from remote sensing images and introduces a novel algorithm, the Global Collinearity-aware Polygonizer (GCP). GCP, built upon an instance segmentation framework, processes binary masks produced by any instance segmentation model. The algorithm begins by collecting polylines sampled along the contours of the binary masks. These polylines undergo a refinement process using a transformer-based regression module to ensure they accurately fit the contours of the targeted building instances. Subsequently, a collinearity-aware polygon simplification module simplifies these refined polylines and generate the final polygon representation. This module employs dynamic programming technique to optimize an objective function that balances the simplicity and fidelity of the polygons, achieving globally optimal solutions. Furthermore, the optimized collinearity-aware objective is seamlessly integrated into network training, enhancing the cohesiveness of the entire pipeline. The effectiveness of GCP has been validated on two public benchmarks for polygonal building mapping. Further experiments reveal that applying the collinearity-aware polygon simplification module to arbitrary polylines, without prior knowledge, enhances accuracy over traditional methods such as the Douglas-Peucker algorithm. This finding underscores the broad applicability of GCP. The code for the proposed method will be made available at this https URL.
Abstract (translated)
本文探讨了从遥感图像中绘制多边形建筑物的挑战，并引入了一种新的算法——全局共线性感知多边形化器（Global Collinearity-aware Polygonizer，简称GCP）。GCP建立在实例分割框架之上，处理由任何实例分割模型生成的二值掩码。该算法首先收集沿二值掩码轮廓采样的折线，然后利用基于变压器的回归模块对这些折线进行细化处理，以确保它们能准确地贴合目标建筑物实例的轮廓。接下来，一个共线性感知多边形简化模块会对经过优化的折线进行简化，并生成最终的多边形表示。此模块采用动态规划技术来优化平衡多边形简洁性和精确性的目标函数，从而实现全局最优解。此外，该优化后的共线性感知目标被无缝集成到网络训练中，增强了整个流程的一致性。 GCP的有效性已在两个用于多边形建筑物映射的公共基准测试上得到了验证。进一步的实验表明，在没有先验知识的情况下，将共线性感知多边形简化模块应用于任意折线可以提高准确性，并优于传统的如道格拉斯-普克算法等方法。这一发现强调了GCP的广泛应用前景。 该方法的代码将在以下链接提供：[此URL]。
URL
https://arxiv.org/abs/2505.01385
