AlphaEarth Foundations (AEF) in Earth Observation: A Systematic Review of Applications and Practices

AlphaEarth Foundations (AEF) unifies multi-modal and multi-temporal observations into analysis-ready low-dimensional representations, introducing a representation-driven paradigm that addresses key limitations of traditional task-centric approaches, including high engineering complexity, limited cross-regional transferability, and strong dependence on labeled data. This paper presents the first systematic review of AEF, synthesizing 23 research articles published up to March 2026 from three perspectives: conceptual positioning, technical framework, and application practices. It first clarifies the distinctions between AEF and related paradigms, including foundation models, remote sensing large models, and existing unified embedding approaches. It then summarizes the organization of its data system and key technical components. Based on a systematic literature survey, the paper further provides a structured synthesis of current studies in terms of thematic and regional distribution, scientific questions, usage patterns, and evaluation methods. The analysis indicates that AEF represents an important step toward a paradigm shift from task-driven to representation-driven Earth observation, offering clear advantages in reducing engineering barriers, enabling cross-regional transfer, and establishing a unified environmental semantic foundation. However, limitations remain in temporal resolution, semantic interpretability, and performance in specific task scenarios. Future work should focus on enhancing dynamic representation capabilities, cross-domain adaptation mechanisms, multi-source integration frameworks, systematic evaluation and annotation systems, and asset-oriented applications. By delineating the capability boundaries and applicable contexts of AEF from both methodological and empirical perspectives, this study provides a systematic reference for the standardized development and rational application of unified surface embeddings as an emerging geospatial information infrastructure.