A generalizable and accessible approach to machine learning with global satellite imagery

Esther Rolf, Jonathan Proctor, Tamma Carleton, Ian Bolliger, Vaishaal Shankar, Miyabi Ishihara, Benjamin Recht and Solomon Hsiang

Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley

Abstract: 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.

Keywords: 46 Information and Computing Sciences (for-2020); 4611 Machine Learning (for-2020); Networking and Information Technology R&D (NITRD) (rcdc); Bioengineering (rcdc); Machine Learning and Artificial Intelligence (rcdc) (search for similar items in EconPapers)
Date: 2021-01-01
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.escholarship.org/uc/item/6fg976bb.pdf;origin=repeccitec (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cdl:agrebk:qt6fg976bb

Access Statistics for this paper

More papers in Department of Agricultural & Resource Economics, UC Berkeley, Working Paper Series from Department of Agricultural & Resource Economics, UC Berkeley Contact information at EDIRC.
Bibliographic data for series maintained by Lisa Schiff ().