梳理地理空间机器学习工具库，助你高效处理遥感数据挑战。
Geospatial Machine Learning Libraries
- 系统分析地理空间机器学习库的演进与核心功能
- 介绍TorchGeo等主流库及其与深度学习框架的集成方式
- 适合遥感研究者、地理信息开发者及开源贡献者参考
近年来，领域专用软件库的兴起推动了机器学习的发展，实现了工作流简化和结果可复现。然而，地理空间机器学习（GeoML）领域面临地球观测数据快速增长与专用工具库发展滞后的矛盾，其独特挑战包括空间分辨率差异、光谱特性、时间频率、数据覆盖范围、坐标系统和文件格式多样性。本文全面综述了GeoML库的演进历程、核心功能与当前生态系统，重点介绍TorchGeo、eo-learn和Raster Vision等主流库的架构设计、支持的数据类型及其与主流机器学习框架的整合方式。同时讨论了数据预处理、时空连接、基准测试及预训练模型的使用方法。通过作物类型分类案例研究展示了实际应用价值。文中强调了软件设计、许可证管理与测试的最佳实践，并指出开放源码地理空间软件治理的重要性，以及基础模型兴起带来的新挑战与未来方向。旨在为从业者、开发者和研究人员提供导航与参与指南。
原文摘要 · Abstract (English)
Recent advances in machine learning have been supported by the emergence of domain-specific software libraries, enabling streamlined workflows and increased reproducibility. For geospatial machine learning (GeoML), the availability of Earth observation data has outpaced the development of domain libraries to handle its unique challenges, such as varying spatial resolutions, spectral properties, temporal cadence, data coverage, coordinate systems, and file formats. This chapter presents a comprehensive overview of GeoML libraries, analyzing their evolution, core functionalities, and the current ecosystem. It also introduces popular GeoML libraries such as TorchGeo, eo-learn, and Raster Vision, detailing their architecture, supported data types, and integration with ML frameworks. Additionally, it discusses common methodologies for data preprocessing, spatial--temporal joins, benchmarking, and the use of pretrained models. Through a case study in crop type mapping, it demonstrates practical applications of these tools. Best practices in software design, licensing, and testing are highlighted, along with open challenges and future directions, particularly the rise of foundation models and the need for governance in open-source geospatial software. Our aim is to guide practitioners, developers, and researchers in navigating and contributing to the rapidly evolving GeoML landscape.
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