Real-time semantic segmentation of remote sensing imagery is a challenging task that requires a trade-off 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) for real-time semantic segmentation in computer vision, researchers have adopted these efficient deep neural networks in remote sensing image analysis. This paper begins with a summary of the fundamental compression methods for designing efficient deep neural networks 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 deep neural networks on a publicly available remote sensing semantic segmentation benchmark dataset, the OpenEarthMap. The experimental results of an extensive comparative study demonstrate that most of the existing efficient deep neural networks have good segmentation quality, but they suffer low inference speed (i.e., 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.

翻译：遥感图像的实时语义分割是一项具有挑战性的任务，需要在效果与效率之间取得平衡。该技术广泛应用于森林火灾监测、土地利用与覆盖变化检测、作物健康监测等多个领域。随着计算机视觉中用于实时语义分割的高效深度学习方法（即高效深度神经网络）取得成功，研究人员已将这些高效深度神经网络应用于遥感图像分析。本文首先总结了设计高效深度神经网络的基本压缩方法，并进行了简要而全面的综述，概述了遥感图像实时语义分割的最新进展。我们审视了若干具有代表性的高效深度学习方法，依据网络架构设计方法将其纳入分类体系。此外，我们在公开的遥感语义分割基准数据集OpenEarthMap上评估了部分现有高效深度神经网络的质量与效率。一项广泛的对比研究实验结果表明，现有的大部分高效深度神经网络具有良好的分割质量，但存在推理速度低（即高延迟率）的问题，这可能会限制其在遥感图像分割实时应用中的部署能力。我们为遥感图像实时语义分割的当前趋势与未来研究方向提供了见解。