Machine Learning Techniques for Enhanced Detection of Underground Infrastructure in Urban Environments
摘要
This research investigates the application of machine learning techniques to enhance the detection of underground infrastructure in urban environments. Our study aims to improve the accuracy, efficiency, and cost-effectiveness of underground infrastructure detection by leveraging the capabilities of machine learning. Through the integration of advanced data processing techniques and geospatial information, we develop novel methodologies for subsurface mapping and localization. We analyze the strengths, limitations, and practical considerations of employing machine learning in underground infrastructure detection. The findings of this research contribute to the advancement of modern detection methods and provide valuable insights for urban planning, infrastructure management, and disaster preparedness efforts.