Advanced Spatial Intelligence and Edge-Driven Artificial Intelligence in Modern Geospatial Engineering: A Comprehensive Review

Abstract

Abstract: The growing combination of geospatial technologies, Artificial Intelligence (AI), and edge computing is changing the field of spatial analysis, environmental monitoring, and infrastructural design. This article gives a thorough summary of the way modern computer science approaches—namely machine learning (ML), deep learning (DL), container orchestration using Kubernetes, and ultra-reliable low-latency communications (URLLC)—are being incorporated into geospatial geoinformatics. Instead of carrying out processing in centralised cloud systems, geospatial systems can now handle high-resolution Earth Observation (EO) data, LiDAR point clouds, and Internet of Things (IoT) spatial streams in near real-time by moving the processing tasks to the network edge. We look systematically at the basic methods involved in spatial intelligence, containerized orchestration, multi-sensor data fusion, and edge deployment architectures. Moreover, we combine the more recent literature from a range of disciplines to show the way in which spatial technologies directly contribute to the UN Sustainable Development Goals (SDGs), help reduce regional environmental degradation, and improve university-based entrepreneurial ecosystems. Lastly, the main research gaps—such as the problem of bandwidth limitations in remote areas, model drift in changing environments, and governance constraints—are identified, together with specific future directions for next-generation spatial computing.