Real-Time Semantic Segmentation: A Brief Survey & Comparative Study in Remote Sensing
Clifford Broni-Bediako, Junshi Xia, and Naoto Yokoya

TL;DR
This survey reviews recent efficient deep learning methods for real-time semantic segmentation in remote sensing, highlighting their strengths and limitations, especially in inference speed, and discusses future research directions.
Contribution
It provides a comprehensive survey of recent methods, a taxonomy based on architecture design, and an extensive comparative evaluation on a benchmark dataset.
Findings
Most methods have good segmentation quality
Existing methods suffer from low inference speed
Speed limitations may hinder real-time deployment
Abstract
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…
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Taxonomy
TopicsRemote-Sensing Image Classification · Advanced Neural Network Applications · Advanced Image Fusion Techniques
MethodsSPEED: Separable Pyramidal Pooling EncodEr-Decoder for Real-Time Monocular Depth Estimation on Low-Resource Settings
