# Semantic Segmentation of Aerial Imagery Using U-Net with Self-Attention and Separable Convolutions
## 1 Introduction  
The accurate semantic segmentation of aerial imagery holds immense significance in various practical applications, particularly in fields such as urban planning and environmental monitoring. As cities expand and landscapes evolve, the need for precise land cover classification becomes paramount for effective urban development and resource management. Furthermore, in environmental monitoring, the ability to differentiate between various land features aids in assessing ecological changes and ensuring sustainable practices. The integration of a U-Net framework with self-attention mechanisms and separable convolutions, as explored in this research, offers a promising avenue for advancing the accuracy of segmentation methods. The practical implications of this research extend to critical decision-making processes, where the improved model can provide detailed insights into urban infrastructure, land use patterns, and environmental changes. Such advancements are crucial for policymakers, urban designers, and environmental scientists who rely on accurate spatial information for informed decision making. By enhancing the precision of semantic segmentation, this study contributes to the broader goal of harnessing aerial imagery for practical applications that address contemporary challenges in urban development and environmental sustainability. The most difficult process that you can come across in the field of computer vision is the problem of semantic segmentation. The problem of semantic segmentation has been practiced and studied heavily in an image processing context. In the study of semantic segmentation, the goal is to connect or classify each pixel of an image into a certain class [1]. Land use and land cover maps arefrequently made from medium- and high-resolution satellite images like Sentinel [2] and Landsat [3]. Semantic segmentation holds crucial significance across a diverse range of applications within the field of geographic information. This technique plays a pivotal role in various geographic information applications, showcasing its multifaceted utility in extracting meaningful insights from spatial data [4]. The application of deep learning (DL) in remote sensing semantic segmentation is progressively gaining traction [5]. Earlier techniques relying on sliding windows and candidate regions are characterized by their time-consuming nature and a substantial number of redundant computations [6]. In recent times, an increasing number of methods based on deep learning (DL) have emerged in the realm of remote sensing, encompassing variations of the U-Net model and its derivatives [7]. By discerning the significance of specific elements within a given context, attention mechanisms contribute to the refinement of decision-making processes. Their ability to ignore extraneous or irrelevant data not only streamlines computational efficiency but also plays a crucial role in mitigating the impact of noise or distractors in complex datasets [8]. The deep learning architecture that is used herein is a self-attention U-Net with separable convolutions. The self-attention takes advantage of focusing on the key pixels in the images while ignoring the unnecessary pixel values. In order to better handle input images with high resolutions, we split input images into image patches and perform training and evaluation in a patch-by-patch manner [9]. The deep learning architecture that is used herein is a self-attention U-Net with separable convolutions. The self-attention takes advantage of focusing on the key pixels in the images while ignoring the unnecessary pixel values. The separable convolutions used in the proposed architecture speed up the training process. Several works in the literature have undertaken comprehensive reviews of research methodologies within the realm of semantic segmentation for remote sensing data. These works systematically classify and explore diverse perspectives, specifically delving into remote sensing image analysis within the broader domain of general deep learning algorithms.