Deep Learning and Earth Observation to Support the Sustainable Development Goals

TL;DR

This paper reviews how deep learning combined with Earth observation data can significantly advance the achievement of sustainable development goals by monitoring key global challenges and providing new solutions.

Contribution

It systematically reviews current deep learning applications in Earth observation for SDGs, highlighting recent case studies and societal implications.

Findings

Deep learning enhances monitoring of SDGs like hunger, urban sustainability, and climate change.

Earth observation data combined with AI can improve biodiversity preservation.

The approach offers promising tools for addressing climate crisis and sustainable development.

Abstract

The synergistic combination of deep learning models and Earth observation promises significant advances to support the sustainable development goals (SDGs). New developments and a plethora of applications are already changing the way humanity will face the living planet challenges. This paper reviews current deep learning approaches for Earth observation data, along with their application towards monitoring and achieving the SDGs most impacted by the rapid development of deep learning in Earth observation. We systematically review case studies to 1) achieve zero hunger, 2) sustainable cities, 3) deliver tenure security, 4) mitigate and adapt to climate change, and 5) preserve biodiversity. Important societal, economic and environmental implications are concerned. Exciting times ahead are coming where algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.