Polygonizer：一种自回归建筑轮廓描绘器
Polygonizer: An auto-regressive building delineator
AI总结：
提出一种Image-to-Sequence自回归模型Polygonizer，直接从遥感图像推断建筑形状并输出矢量格式，避免了传统语义分割的复杂后处理，在使用真实边界框时实现了最低的最大切线角度误差。
摘要：
在地理空间规划中，将对象表示为矢量化格式通常是必不可少的，因为这种格式能轻松转化到Web开发、图形或设计等下游任务。虽然这些问题常使用语义分割来解决，但这需要额外的后处理以复杂的方式将对象矢量化，我们提出了一种Image-to-Sequence模型，允许直接进行形状推断，并开箱即用适用于基于矢量的工作流。我们以多种方式展示了模型的性能，包括对图像输入施加扰动，这些扰动对应于遥感应用中常见的变异或伪影。当使用真实边界框（每张图像一个对象）时，我们的模型优于先前的工作，实现了最低的最大切线角度误差。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
