从机器学习到大规模地球观测产品：制作地图的最佳实践
From Machine Learning to Large-Scale EO Products: Best Practices for Making Maps
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中文总结 AI 辅助
本文探讨从机器学习到大规模EO产品制作地图的最佳实践，围绕EO数据基础设施、数据处理、模型训练、不确定性量化、地图制作与分发及验证等六个主题，给出从卫星数据到运营地图产品整个流程的推荐做法。
中文摘要 AI 辅助
近年来，受机器学习和大型计算基础设施进步推动，源自地球观测（EO）数据的大规模地理空间地图制作迅速发展。尽管生成此类地图的障碍大幅下降，但尚未形成既定的最佳实践，早期流程中的设计决策可能将错误引入最终产品。每个阶段的选择紧密相关，本文介绍了从卫星数据到运营地图产品整个流程的推荐实践，围绕六个相互关联的主题展开讨论，包括EO数据基础设施格局、数据选择与预处理、机器学习数据集构建与模型训练、不确定性量化、地图制作与分发以及验证。
英文摘要
Recent years have seen a rapid expansion in the production of large-scale geospatial maps derived from Earth observation (EO) data, driven largely by advances in machine learning (ML) and large computing infrastructure. Although the barrier to generating such maps has dropped substantially, established best practices have yet to emerge, and design decisions made early in the pipeline can quietly propagate errors into the final product. Producing a technically sound and scientifically credible product remains challenging. Choices made at every stage are tightly coupled: preprocessing decisions shape the training signal, dataset design governs what the model can learn and how reliably its performance can be assessed, and global-scale inference introduces engineering challenges in compute and data access at scale, as well as artifact mitigation. Furthermore, uncertainty quantification and independent map validation each require dedicated methodological attention that is often underestimated. This paper presents a concise, end-to-end account of the recommended practices spanning the pipeline from satellite data to an operational map product. We organize the discussion around six interconnected themes: the EO data infrastructure landscape, data selection and preprocessing, ML dataset construction and model training, uncertainty quantification, map production and distribution, and validation. This paper is a condensed version of a longer guide that provides greater depth across all stages, accessible online at ghjuliasialelli.github.io/ML-EO-Maps/.
发表机构
- ETH Zurich（苏黎世联邦理工学院）
- ETH AI Center（苏黎世联邦理工学院人工智能中心）
- University of Cambridge（剑桥大学）
- Swiss Federal Research Institute WSL（瑞士联邦森林、雪和景观研究所）
- Asterisk Labs（星号实验室）
- EcoVision Lab, University of Zurich（苏黎世大学生态视觉实验室）
- Leipzig University（莱比锡大学）
- TU Munich（慕尼黑工业大学）
- RISE Research Institutes of Sweden（瑞典皇家理工学院）
机构由 AI 辅助整理，请以论文原文为准。
