The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Author: Zhenlei Song (Texas A&M University)
Advisor: Zhe Zhang (Texas A&M University)
Abstract: Spatial decision support systems (SDSS) are pivotal in resolving complex geospatial challenges but face critical limitations in harmonizing conflicting objectives, capturing behavioral heterogeneity, and enabling efficient large-scale data processing. Besides, a central challenge is that current Geospatial cyberinfrastructure (GeoCI) is inefficient in supporting real-time data access. Additionally, CI struggles to deliver scalable computations needed for addressing complex, multidimensional problems. This research addressed these limitations by presenting a unified GeoCI framework powered by geospatial artificial intelligence (GeoAI). Our framework provides a dual-capability platform, enabling both participatory, stakeholder-driven planning for scenarios like offshore wind siting, and high-fidelity, agent-based simulations for dynamic phenomena such as epidemic transmission. These sophisticated applications are underpinned by an intelligent service tier where a machine learning model reduces data retrieval times by over 80%, making the entire system more scalable and responsive. This study provides a validated template for building next-generation spatial data sharing systems (SDSS) that can balance the needs of all stakeholders, simulate complex real-world dynamics, and process massive geospatial datasets in real time, thereby achieving the goals of greater efficiency, inclusiveness, and adaptability to the complexities of the real world.
Thesis Canvas: pdf