A Generalizable and Accessible Approach to Machine Learning with Global Satellite Imagery

TL;DR

This paper introduces a resource-efficient, generalizable satellite imagery encoding method that enables diverse socioeconomic and environmental predictions globally, with high accuracy and low computational cost, accessible to researchers worldwide.

Contribution

The authors present a universal satellite image encoding that generalizes across tasks, reducing computational requirements and enabling widespread, accessible machine learning applications.

Findings

Achieves accuracy comparable to deep neural networks

Scales globally with low computational cost

Enables label super-resolution and uncertainty estimation

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.