Semantic Segmentation in Aerial Imagery: A Novel Approach for Urban Planning and Development

Pawan Chinnari, Soumen Sinha, Budonkayala Ishaa, Neha Bharill, Om Prakash Patel

Abstract: Urban planning faces increasingly complex challenges, marked by rapid urbanization and the need for sustainable development. Recognizing these challenges, our research focuses on semantic segmentation in aerial imagery. In our research, we identify challenges such as inadequate delineation of environmental features and the difficulty of obtaining critical insights from aerial imagery. To tackle these issues, we propose a state-of-the-art solution employing a modified Inception-ResNet-V2 U-Net architecture. Our research encompasses a comprehensive methodology that includes meticulous dataset preparation, bespoke model architecture design, refined loss function optimization, and robust training protocols augmented by data enrichment techniques. Our results showcase an impressive model accuracy of 84.6%, underlining the method's superior performance in accurately delineating environmental features from aerial imagery. This land use and land cover classification empowers urban planners and developers with critical insights, facilitating informed decision-making and sustainable urban development. Our paper also focuses on contour detection performed on natural vegetation which is one of the crucial aspect for urban planning. This approach offers a tool that can be used in real time for urban planning and improved accuracy of environmental feature delineation.