The ability to transform location-centric geospatial data into meaningful computational representations has become fundamental to modern spatial analysis and decision-making. Geospatial Representation Learning (GRL), the process of automatically extracting latent structures and semantic patterns from geographic data, is undergoing a profound transformation through two successive technological revolutions: the deep learning breakthrough and the emerging large language model (LLM) paradigm. While deep neural networks (DNNs) have demonstrated remarkable success in automated feature extraction from structured and semi-structured geospatial data (e.g., satellite imagery, GPS trajectories), the recent integration of LLMs introduces transformative capabilities for cross-modal geospatial reasoning and unstructured geo-textual data processing. This survey presents a comprehensive review of geospatial representation learning across both technological eras, organizing them into a structured taxonomy based on the complete pipeline comprising: (1) data perspective, (2) methodological perspective, and (3) application perspective. We also highlight current advancements, discuss existing limitations, and propose potential future research directions in the LLM and foundation model era. This work offers a thorough exploration of the field and provides a roadmap for further innovation in GRL. The summary of the up-to-date paper list can be found in https://github.com/CityMind-Lab/Awesome-Geospatial-Representation-Learning and will undergo continuous updates.

翻译：将位置中心的地理空间数据转化为有意义的计算表征的能力，已成为现代空间分析与决策的基础。地理空间表征学习（GRL），即从地理数据中自动提取潜在结构和语义模式的过程，正经历着两次连续技术革命带来的深刻变革：深度学习突破与新兴的大语言模型（LLM）范式。虽然深度神经网络（DNNs）在从结构化与半结构化地理空间数据（如卫星影像、GPS轨迹）中自动提取特征方面已展现出显著成功，但近期LLMs的整合为跨模态地理空间推理及非结构化地理文本数据处理引入了变革性能力。本综述全面回顾了跨越这两个技术时代的地理空间表征学习，并基于包含以下环节的完整流程将其组织成一个结构化的分类体系：（1）数据视角，（2）方法视角，以及（3）应用视角。我们还重点介绍了当前进展，讨论了现有局限，并在LLM与基础模型时代提出了潜在的未来研究方向。本工作对该领域进行了深入探讨，并为GRL的进一步创新提供了路线图。最新论文列表汇总可在 https://github.com/CityMind-Lab/Awesome-Geospatial-Representation-Learning 找到，并将持续更新。