Bridging the gap: deep learning in the semantic segmentation of remote sensing data

Abstract

Semantic segmentation has crucial importance in various domains due to its ability to recognize and categorize objects within an image at a pixel level. This task enables a wide range of applications, such as autonomous vehicles, environmental monitoring, and remote sensing (RS). In RS, semantic segmentation plays a crucial role, acting as the basis for applications including land cover classification. Following the success of deep learning (DL) methods in computer vision, our paper addresses the intersection between DL and RS imagery. We focus on improving the efficiency of some baseline and backbone models to ensure their adaptability to the challenges posed by RS imagery. Therefore, we evaluate state-of-the-art models on two datasets and investigate their ability to accurately segment objects in RS imagery. Our research aims to open the way for more accurate and reliable semantic segmentation methods in geospatial analysis.