Building and road detection are fundamental tasks in remote sensing and geospatial analysis, with applications ranging from urban planning to disaster management. Traditional methods for building and road detection often rely on handcrafted features and complex rule-based algorithms, which may struggle to handle the variability and complexity of real-world scenarios. In recent years, deep learning techniques have emerged as a powerful approach for automating and enhancing building detection tasks. which shows the potential to handle complex patterns and adapt to various imaging conditions., However, the state of art deep learning algorithm YOLOv8 exhibits limitations in achieving precise localization when it comes to detecting smaller objects. In light of this ,We propose an enhanced YOLOv8-based semantic segmentation algorithm, incorporating a Convolutional Block Attention Module (CBAM) into the network. Results demonstrate the algorithm's effectiveness in automating building and road recognition with minimal human intervention, significantly improving accuracy even with limited training rounds.
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