A recent tutorial outlines a step-by-step process for using artificial intelligence to extract building footprints from high-resolution aerial images. The tutorial focuses on configuring a geospatial deep learning environment, preparing image data, and training a U-Net model to segment and identify building footprints. The process involves using various machine learning models, including U-Net, DINO, SAM, and Mask R-CNN. This tutorial aims to provide a comprehensive guide for individuals looking to develop their skills in GeoAI. The development of such AI-powered mapping tools could lead to more accurate and efficient urban planning and infrastructure management.