用自回归变换器直接生成多边形，端到端搞定遥感建筑矢量化。
GeoFormer: A Multi-Polygon Segmentation Transformer
- 将关键点建模为空间相关令牌，自回归生成多边形。
- 在卫星图像上分割建筑精度优于现有方法。
- 首次成功应用自回归变换器做遥感多边形预测，适合遥感矢量化任务。
在遥感领域，学习建筑物等对象的尺度不变形状是一个常见需求。以往方法依赖调整多个损失函数将分割图转换为最终的尺度不变表示，需要繁琐的设计与优化。为此，我们提出GeoFormer，一种新型架构，可端到端学习生成多边形。通过将关键点建模为具有空间依赖性的令牌，并以自回归方式生成，GeoFormer在从卫星图像中描绘建筑物方面表现更优。我们通过多种参数消融实验评估了GeoFormer的鲁棒性，并强调优化单一似然函数的优势。本研究首次成功将自回归变换器模型应用于遥感领域的多边形预测，为建筑矢量化提供了一种有前景的方法论替代方案。
原文摘要 · Abstract (English)
In remote sensing there exists a common need for learning scale invariant shapes of objects like buildings. Prior works relies on tweaking multiple loss functions to convert segmentation maps into the final scale invariant representation, necessitating arduous design and optimization. For this purpose we introduce the GeoFormer, a novel architecture which presents a remedy to the said challenges, learning to generate multipolygons end-to-end. By modeling keypoints as spatially dependent tokens in an auto-regressive manner, the GeoFormer outperforms existing works in delineating building objects from satellite imagery. We evaluate the robustness of the GeoFormer against former methods through a variety of parameter ablations and highlight the advantages of optimizing a single likelihood function. Our study presents the first successful application of auto-regressive transformer models for multi-polygon predictions in remote sensing, suggesting a promising methodological alternative for building vectorization.
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