Deep-Learning-Based Semantic Segmentation for Remote Sensing: A Bibliometric Literature Review

Abstract

Deep learning (DL) has emerged as a powerful technique for a wide range of computer vision applications. Consequently, DL is also being adopted to process geospatial and remote sensing (RS) images. As these methods are sporadic over different studies, many review papers have also been published to gather the approaches and summarize the existing models in this field. However, a state-of-the-art review paper is still scarce in this field that will present a bibliometric analysis as well as a critical analysis of the recent works. Therefore, this article aims to spur the researchers with a bibliometric analysis to identify the current research trend. As a research sample, in total, 281 related papers were collected from the Web of Science source, and bibliometric analysis was accomplished using VOSviewer software. Among the collection of associated works from the database, 28 papers were selected according to the defined criteria for detailed analysis. Besides this, a few research questions were generated to extract necessary information from the literature for extracting the pros and cons of the selected works. DL techniques were applied in these works and achieved results. Furthermore, the papers were also categorized based on the addressed RS application domain.