Journal
NATURE COMMUNICATIONS
Volume 12, Issue 1, Pages -Publisher
NATURE RESEARCH
DOI: 10.1038/s41467-021-24638-z
Keywords
-
Categories
Funding
- NSF [DGE 1752814]
- US Environmental Protection Agency Science To Achieve Results Fellowship Program [FP91780401]
- Harvard Center for the Environment
- Harvard Data Science Initiative
- Sloan Foundation
- NSF Research Traineeship Program Data Science for the 21st Century
Ask authors/readers for more resources
The paper introduces MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.
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 satellite imagery can generalize across diverse prediction tasks (e.g., forest cover, house price, road length). Our method achieves accuracy competitive with deep neural networks at orders of magnitude lower computational cost, scales globally, delivers label super-resolution predictions, and facilitates characterizations of uncertainty. Since image encodings are shared across tasks, they can be centrally computed and distributed to unlimited researchers, who need only fit a linear regression to their own ground truth data in order to achieve state-of-the-art SIML performance. This paper presents MOSAIKS, a system for planet-scale prediction of multiple outcomes using satellite imagery and machine learning (SIML). MOSAIKS generalizes across prediction domains and has the potential to enhance accessibility of SIML across research disciplines.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available