发表机构
Wroclaw University of Science and Technology（弗罗茨瓦夫理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出LEON，一种基于H3六边形网格和图掩码自编码器的自监督框架，利用OSM设施分布学习空间嵌入，在EuroSAT分类和地理预测任务上显著提升性能。
AI 中文摘要
地理信息系统日益依赖复杂的空间表示学习技术，以从复杂的地理空间数据中提取有意义的模式。本文介绍了LEON，一种新颖的自监督框架，它将图掩码自编码器（GraphMAE）适配于地理空间区域表示学习。我们的方法利用地理数据固有的空间结构，通过H3索引构建六边形网格图，并应用掩码自编码技术，从OpenStreetMap（OSM）设施分布模式中学习稳健的空间嵌入。我们在多个真实世界数据集上评估LEON，包括EuroSAT卫星图像分类以及各种地理预测任务（房价预测、犯罪预测和城市分析）。实验结果表明，LEON在空间理解方面取得了显著改进，在EuroSAT分类上准确率提升高达1.87%，并在地理预测基准上持续获得性能提升。学习到的嵌入表现出高度结构化和独特性质，使其特别适用于下游空间分析任务。我们的研究结果表明，自监督学习为利用广泛可用的众包数据进行地理空间区域表示学习提供了一种有效范式。
英文摘要
Geographic information systems increasingly rely on sophisticated spatial representation learning techniques to extract meaningful patterns from complex geospatial data. This paper introduces LEON, a novel self-supervised framework that adapts Graph Masked Autoencoders (GraphMAE) for geospatial region representation learning. Our method leverages the inherent spatial structure of geographic data by constructing hexagonal grid graphs using H3 indexing and applying masked autoencoding techniques to learn robust spatial embeddings from OpenStreetMap (OSM) amenity distribution patterns. We evaluate LEON on multiple real-world datasets including EuroSAT satellite imagery classification and various geographic prediction tasks (housing prices, crime prediction, and urban analytics). Experimental results demonstrate that LEON achieves significant improvements in spatial understanding, with up to 1.87% accuracy improvement on EuroSAT classification and consistent performance gains across geographic prediction benchmarks. The learned embeddings exhibit highly structured and distinct properties, making them particularly suitable for downstream spatial analysis tasks. Our findings suggest that self-supervised learning provides an effective paradigm for geospatial region representation learning using widely available crowdsourced data.
Comments15 pages, 5 figures, 5 tables. Pre-review preprint version of a paper published in Advances in Computational Collective Intelligence (ICCCI 2026), CCIS 3044, Springer, pp. 274-287
Journal refAdvances in Computational Collective Intelligence, ICCCI 2026, Communications in Computer and Information Science, vol. 3044, Springer, pp. 274-287 (2027)
DOI：10.1007/978-3-032-37936-8_19
