Integrating GIS, Spatial Analysis, and AI for Sustainable Energy and Bioresource Systems

Dear Colleagues,

The transition toward sustainable energy systems has intensified the need for advanced analytical tools that can support efficient planning, deployment, and management of renewable energy resources. This Special Issue welcomes high-quality contributions that explore cutting-edge methodologies integrating Geographic Information Systems (GIS), spatial analysis, and artificial intelligence (AI) to address emerging challenges in the renewable energy sector. It also welcomes research articles in the domains of biofuels, environmental catalysis, and energy, particularly those that incorporate spatial analysis of resource distributions and spatially explicit assessments.

Recent progress in geospatial technologies—ranging from high-resolution remote sensing data to real-time environmental monitoring—has enabled more accurate assessments of solar, wind, hydro, geothermal, and bioenergy potential. In parallel, AI techniques such as machine learning, deep learning, and spatial decision-support algorithms enhance predictive modeling, optimize site selection, and improve energy production forecasting. The combination of GIS-based spatial analytics with AI-driven approaches offers unprecedented opportunities for identifying optimal locations for renewable infrastructure, analyzing spatial–temporal variability, assessing environmental impacts, and supporting adaptive energy management under climate change scenarios.

This Special Issue welcomes research articles, reviews, and case studies that highlight technological innovations, novel workflows, and interdisciplinary applications. Topics include but are not limited to spatial modeling, resource mapping, multi-criteria decision analysis, AI-enhanced geospatial forecasting, and integrated frameworks for sustainable renewable energy planning.

Dr. Sotiris Lycourghiotis
Prof. Dr. Kyriakos Bourikas
Guest Editors

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