An Overview of Deep Learning Techniques for Road Detection in High-Resolution Satellite Images

Abstract

One of the main and most difficult problems in computer vision research has been removing road networks from high-resolution remote sensing photos. Numerous applications, such as urban planning, traffic management, disaster response, and environmental monitoring, depend on the accurate identification of road networks. The field of artificial intelligence has witnessed tremendous advancements and advances in road extraction technology, especially in the area of deep learning. This research provides an extensive analysis of deep learning techniques for road extraction from remote sensing imagery, emphasizing how these technologies improve process accuracy and efficiency. Deep learning techniques are divided into fully supervised, semi-supervised, and unsupervised learning approaches, each having more focused subcategories, depending on the kind of annotated data that is used. The tenets, advantages, and disadvantages of these approaches are contrasted and examined. The review also provides an overview of the metrics applied to assess road extraction algorithms and the high-resolution remote sensing picture datasets used for these kinds of activities. In conclusion, we go over the main obstacles and potential applications of computational intelligence methods to enhance the precision, automation, and intelligence of road network extraction.