  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.
