11 Mathematical Optimization for Enhanced AI-Enabled Geospatial Intelligence

In the era of big data, the integration of artificial intelligence (AI) with geospatial intelligence (GEOINT) has revolutionized the way we analyze, interpret, and utilize geospatial data. This chapter delves into the application of mathematical optimization techniques to enhance AI-enabled GEOINT, offering both theoretical insights and practical implementations. By combining optimization algorithms with AI models, this chapter demonstrates how these advanced techniques can solve complex geospatial problems, such as route optimization, disaster response, and urban planning. Besides the theoretical foundations of mathematical optimization and its impacts on AI (in a separate chapter), we cover practical use-cases too, with hands-on examples that show how these tools can be applied in the real world. The important part of practical implementation guides them by methods for the anticipation, running, and execution subsequent to combining information, model building, and previously official stages in developing this as a chapter that is of great benefit for investigators, aspirants, and those studying from sizable spheres, AI analysts or practitioners, plus scholars in neuroscience. The modern status of the field is described, and some challenges as well as future trends are presented, thereby underlining that this interdisciplinary scientific domain has not yet come to a standstill.