The recent developments in the research area of geospatial intelligence and smart systems demonstrate a significant shift towards leveraging advanced technologies such as artificial intelligence (AI), Internet of Things (IoT), and distributed computing to address complex challenges in urban planning, environmental monitoring, and resource management. A notable trend is the integration of multimodal data sources, including remote sensing, street view imagery, and real-time sensor data, to enhance the accuracy and timeliness of geospatial analyses. This approach is particularly evident in systems designed for economic development tracking and smart parking solutions, where the fusion of diverse data types has led to more efficient and scalable solutions. Additionally, there is a growing emphasis on user-friendly, open-source tools that democratize access to sophisticated geospatial analysis capabilities, enabling broader research communities to process large-scale data with reduced computational barriers. These advancements not only streamline existing workflows but also open new avenues for predictive modeling and decision-making in various domains. Notably, the development of AI-driven smart spaces and traffic management systems highlights the potential for AI to transform urban environments by optimizing resource utilization, enhancing user comfort, and improving overall safety and efficiency. As the field continues to evolve, future research is likely to focus on further integrating AI methodologies with traditional geospatial techniques to address emerging challenges and capitalize on new opportunities.