A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery
收藏NBER2020-11-01 更新2025-01-04 收录
下载链接：
https://www.nber.org/papers/w28045
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资源简介：
Combining satellite imagery with machine learning (SIML) has the potential to address global challenges by remotely estimating socioeconomic and environmental conditions in data-poor regions, yet the resource requirements of SIML limit its accessibility and use. We show that a single encoding of
提供机构：
美国国家经济研究局
创建时间：
2020-11-01
