Harnessing Earth Observation and Machine Learning for Sustainable Development Goals

Abstract

The integration of deep learning models with remote sensing promises significant progress in advancing sustainable development goals. New advances and a myriad of applications are already changing the way mankind will face the living planet challenges. This article reviews the current Vision‐language and foundational models for remote sensing data, along with their application toward monitoring and achieving the Sustainable Development Goals most impacted by the rapid development of deep learning in Earth observation. We systematically review case studies to (1) achieve zero hunger, (2) clean water and sanitation, and (3) mitigate and adapt to climate change. Important societal, economic, and environmental implications are of concern. Exciting times are coming where algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development. This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data Mining Application Areas > Science and Technology Technologies > Machine Learning