Low earth orbit (LEO) satellite networks are emerging as a key infrastructure for global connectivity and space-based sensing. Many tasks in such systems can be formulated as measurement-set-to-spatial-inference problems, where spatial variables are inferred from sparse and heterogeneous wireless observations. Spectrum cartography provides a unifying framework for this paradigm, encompassing representative tasks such as satellite-assisted localization and radio map reconstruction, as well as map-informed resource allocation. Yet the highly dynamic orbital geometry, complex propagation conditions, and reliability-varying nature of LEO measurements pose fundamental challenges for traditional model-driven and interpolation-based methods. This article surveys the literature from 1964 to 2026 on learning-based spectrum cartography as applied to LEO satellite networks, with a particular focus on attention mechanisms as a principled operator for adaptive and reliability-aware measurement fusion across localization, radio map reconstruction, and resource allocation tasks. We review modeling foundations and key challenges of representative tasks, and analyze how attention-based learning enables flexible fusion of heterogeneous measurements for both inference and map-informed decision-making. Representative formulations and simulation studies are provided to illustrate the framework and demonstrate its effectiveness, offering a unified perspective for measurement-driven inference and decision-making in LEO satellite networks.

翻译：低地球轨道（LEO）卫星网络正成为全球连接和天基感知的关键基础设施。此类系统中的许多任务可归结为从稀疏且异构的无线观测中推断空间变量的测量-空间推断问题。频谱地图构建为该范式提供了统一框架，涵盖卫星辅助定位、无线电地图重建以及基于地图的资源分配等代表性任务。然而，高度动态的轨道几何、复杂的传播条件以及LEO观测在可靠性上的多变性，对传统的模型驱动方法和基于插值的方法构成了根本性挑战。本文综述了1964年至2026年间应用于LEO卫星网络的基于学习的频谱地图构建文献，特别关注注意力机制，将其视为一种原则性算子，用于在定位、无线电地图重建和资源分配任务中实现自适应且考虑可靠性的测量融合。我们回顾了代表性任务的建模基础与关键挑战，并分析了基于注意力的学习如何实现异构测量值的灵活融合，以支持推断和基于地图的决策。文章提供了代表性的公式推导与仿真研究，以说明该框架并展示其有效性，从而为LEO卫星网络中基于测量的推断与决策提供统一视角。