Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a tradeoff between effectiveness and efficiency. It has many applications, including tracking forest fires, detecting changes in land use and land cover, crop health monitoring, and so on. With the success of efficient deep learning methods [i.e., efficient deep neural networks (DNNs)] for real-time semantic segmentation in computer vision, researchers have adopted these efficient DNNs in remote sensing image analysis. This article begins with a summary of the fundamental compression methods for designing efficient DNNs and provides a brief but comprehensive survey, outlining the recent developments in real-time semantic segmentation of remote sensing imagery. We examine several seminal efficient deep learning methods, placing them in a taxonomy based on the network architecture design approach. Furthermore, we evaluate the quality and efficiency of some existing efficient DNNs on a publicly available remote sensing semantic segmentation benchmark dataset, OpenEarthMap. The experimental results of an extensive comparative study demonstrate that most of the existing efficient DNNs have good segmentation quality, but they suffer low inference speed (i.e., a high latency rate), which may limit their capability of deployment in real-time applications of remote sensing image segmentation. We provide some insights into the current trend and future research directions for real-time semantic segmentation of remote sensing imagery.

中文翻译：

---

实时语义分割：遥感领域的简要调查与比较研究

遥感图像的实时语义分割是一项具有挑战性的任务，需要在有效性和效率之间进行权衡。它有很多应用，包括跟踪森林火灾、检测土地利用和土地覆盖的变化、作物健康监测等。随着计算机视觉中实时语义分割的高效深度学习方法[即高效深度神经网络（DNN）]的成功，研究人员已在遥感图像分析中采用这些高效的DNN。本文首先总结了设计高效 DNN 的基本压缩方法，并提供了简短而全面的调查，概述了遥感图像实时语义分割的最新发展。我们研究了几种开创性的高效深度学习方法，将它们置于基于网络架构设计方法的分类中。此外，我们在公开的遥感语义分割基准数据集 OpenEarthMap 上评估了一些现有高效 DNN 的质量和效率。大量比较研究的实验结果表明，大多数现有的高效 DNN 都具有良好的分割质量，但它们的推理速度较低（即高延迟率），这可能会限制它们在远程实时应用中的部署能力。传感图像分割。我们对遥感图像实时语义分割的当前趋势和未来研究方向提供了一些见解。