Traditional methods of geospatial analysis often rely on manual identification and mapping of objects within geographical regions. These methods are not only time-consuming and labor-intensive but are also prone to errors, especially when applied to large-scale datasets. Additionally, they often lack the scalability required for handling high-resolution satellite imagery or extensive geographic areas.

The limitations of these traditional approaches are particularly evident in applications requiring rapid and accurate decision-making, such as disaster response, urban planning, and environmental monitoring. The inability to efficiently process and analyze geospatial data can lead to delays, inaccuracies, and missed opportunities for actionable insights.

Therefore, there is an urgent need for automated solutions that integrate object detection and geographic coordinate calculations. Such solutions can streamline geospatial analysis by leveraging advanced algorithms to detect, locate, and map objects with high accuracy and scalability. By addressing these challenges, automated systems can unlock new possibilities for geospatial research and practical applications.