The comparison of deep learning-based semantic segmentation of road extraction in high-resolution remote sensing images: meta analysis

Abstract

Extracting roads from high-resolution remote sensing images has been a popular and challenging topic, and with the rapid rise of deep learning, applying semantic segmentation methods to extract road networks is becoming increasingly popular. This paper systematically and comprehensively analyze almost all relevant studies on applying deep learning semantic segmentation methods for road extraction of high-resolution and very high-resolution remote sensing images since FCN was proposed by meta-analysis methods. As the statistical analysis progressed, we statistically analyzed the publication status, research space distribution, and citation frequency of papers in this field. And then we describe and summarize the key points that have generally received attention in papers in this area, including the model algorithm architecture, the backbone network used, the loss function, the dataset, the experimental implementation details, the evaluation metrics and quantitative comparison of models. Finally, we provide an outlook on potential future research hotspots based on the results of our analysis.