The synergistic combination of deep learning (DL) models and Earth observation (EO) 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 challenges of our planet. This article reviews current DL approaches for EO data, along with their applications toward monitoring and achieving the SDGs most impacted by the rapid development of DL in EO. We systematically review case studies to achieve zero hunger, create sustainable cities, deliver tenure security, mitigate and adapt to climate change, and preserve biodiversity. Important societal, economic, and environmental implications are covered. Exciting times are coming when algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development.

中文翻译：

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支持可持续发展目标的深度学习和地球观测：当前方法、开放挑战和未来机遇

深度学习 (DL) 模型和地球观测 (EO) 的协同组合有望在支持可持续发展目标 (SDG) 方面取得重大进展。新的发展和大量的应用已经在改变人类面对地球挑战的方式。本文回顾了当前用于 EO 数据的 DL 方法，以及它们在监测和实现受 EO 中 DL 快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，以实现零饥饿、创建可持续城市、提供权属保障、缓解和适应气候变化以及保护生物多样性。涵盖了重要的社会、经济和环境影响。