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.
翻译:新的发展和大量应用已经改变了人类面对地球生存挑战的方式。本文件回顾了地球观测数据目前的深层次学习方法及其在监测和实现受地球观测中深层学习迅速发展影响最大的可持续发展目标方面的应用。我们系统地审查案例研究,以便(1) 实现零饥饿,(2) 可持续城市,(3) 提供保有权保障,(4) 减轻和适应气候变化,(5) 保护生物多样性。重要的社会、经济和环境影响正在引起人们的注意。各种算法和地球数据将在今后推动我们应对气候危机和支持更可持续发展的努力。