GeoAI, or Geospatial Artificial Intelligence, enhances the capabilities of traditional Geographic Information Systems (GIS) by integrating artificial intelligence and machine learning to process and analyze large volumes of spatial data more efficiently and accurately. Unlike traditional GIS, which focuses on visualizing spatial data, GeoAI uncovers patterns, makes predictions, and automates tasks such as feature extraction and classification. It leverages technologies like deep learning for object detection, vector digitization for converting images to vector data, and change detection for identifying modifications over time. GeoAI's practical applications span environmental monitoring, urban planning, agriculture, transportation, logistics, and disaster response, where it improves efficiency, accuracy, and cost-effectiveness. The system relies on comprehensive geospatial datasets, real-time satellite imagery, and high-performance computing while emphasizing data governance and ethical considerations. Integration with GIS platforms allows organizations to transform GeoAI outputs into actionable insights and spatial experiences, as demonstrated by Felt AI's mapping solutions.