Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce
中文摘要
探讨在地理空间图像丰富但实地标注稀缺且昂贵时，如何训练机器学习模型。
English Summary
Explore training geospatial machine learning models when imagery is abundant but field labels are scarce and expensive.
Original Excerpt
When images, mosaics, and data cubes exist in abundance, but field labels are expensive, rare, and imperfect. The post Small Data, Big Maps: Training Geospatial ML Models When Samples Are Scarce appeared first on Towards Data Science.