Deep Neural Networks (DNNs) learn representation from data with an impressive capability, and brought important breakthroughs for processing images, time-series, natural language, audio, video, and many others. In the remote sensing field, surveys and literature revisions specifically involving DNNs algorithms’ applications have been conducted in an attempt to summarize the amount of information produced in its subfields. Recently, Unmanned Aerial Vehicle (UAV)-based applications have dominated aerial sensing research. However, a literature revision that combines both “deep learning” and “UAV remote sensing” thematics has not yet been conducted. The motivation for our work was to present a comprehensive review of the fundamentals of Deep Learning (DL) applied in UAV-based imagery. We focused mainly on describing the classification and regression techniques used in recent applications with UAV-acquired data. For that, a total of 232 papers published in international scientific journal databases was examined. We gathered the published materials and evaluated their characteristics regarding the application, sensor, and technique used. We discuss how DL presents promising results and has the potential for processing tasks associated with UAV-based image data. Lastly, we project future perspectives, commentating on prominent DL paths to be explored in the UAV remote sensing field. This revision consisting of an approach to introduce, commentate, and summarize the state-of-the-art in UAV-based image applications with DNNs algorithms in diverse subfields of remote sensing, grouping it in the environmental, urban, and agricultural contexts.

深度神经网络 (DNN) 具有令人印象深刻的能力从数据中学习表示，并为处理图像、时间序列、自然语言、音频、视频等带来了重要突破。在遥感领域，已经进行了专门涉及 DNN 算法应用的调查和文献修订，试图总结其子领域产生的信息量。最近，基于无人机 (UAV) 的应用主导了航空传感研究。然而，结合“深度学习”和“无人机遥感”主题的文献修订尚未进行。我们工作的动机是全面回顾在基于无人机的图像中应用的深度学习 (DL) 的基础知识。我们主要专注于描述最近应用中使用无人机获取数据的分类和回归技术。为此，共审查了在国际科学期刊数据库中发表的 232 篇论文。我们收集了已发表的材料，并评估了它们在应用、传感器和所用技术方面的特性。我们讨论了 DL 如何呈现有希望的结果，并具有处理与基于无人机的图像数据相关的任务的潜力。最后，我们预测了未来的前景，评论了无人机遥感领域有待探索的重要深度学习路径。本次修订包括一种方法，介绍、评论和总结基于无人机的图像应用的最新技术，在遥感的不同子领域使用 DNN 算法，将其分组在环境、城市、