小数据，大地图：样本稀缺时训练地理空间机器学习模型
英文摘要
This article discusses training geospatial machine learning models when labeled samples are scarce. It addresses the common problem in remote sensing where imagery is abundant but field labels are expensive and rare. The author likely covers techniques such as transfer learning, self-supervised learning, or data augmentation to overcome label scarcity. The tutorial aims to help practitioners build accurate maps with limited ground truth data.
中文摘要
本文讨论了当标记样本稀缺时如何训练地理空间机器学习模型。它解决了遥感中常见的影像丰富但现场标签昂贵且稀少的问题。作者可能涵盖了迁移学习、自监督学习或数据增强等技术来克服标签稀缺。本教程旨在帮助从业者利用有限的真值数据构建精确的地图。
关键要点
Geospatial ML faces a mismatch between abundant imagery and scarce field labels.
地理空间机器学习面临丰富影像与稀缺现场标签之间的不匹配。
Field labels are expensive, rare, and often imperfect.
现场标签昂贵、稀有且经常不完美。
The tutorial presents methods to train models with limited labeled samples.
本教程介绍了使用有限标记样本训练模型的方法。
Techniques may include transfer learning and self-supervised learning.
技术可能包括迁移学习和自监督学习。