Generative AI in Graph-Based Spatial Computing: Techniques and Use Cases
DOI:
https://doi.org/10.32628/IJSRST24112135Keywords:
Generative AI, Spatial Computing, Graph-Based Computing, Graph Theory, Spatial Data Modeling, AI Techniques, Machine Learning, Predictive Models, Geospatial Applications, Graph Neural NetworksAbstract
Generative AI has proven itself as an efficient innovation in many fields including writing and even analyzing data. For spatial computing, it provides a potential solution for solving such issues related to data manipulation and analysis within the spatial computing domain. This paper aims to discuss the probabilities of applying generative AI to graph-based spatial computing; to describe new approaches in detail; to shed light on their use cases; and to demonstrate the value that they add. This technique thus incorporates graph theory, generative models to model spatial relations, generate new spatial forms and improve on spatial decision-making processes. The paper surveys such methods, describes typical applications, and outlines further development of the subject.
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