A Powerful Approach in Visualization: Creating Photorealistic Landscapes with AI
Gusztáv Jakab,
Enikő Magyari,
Benedek Jakab and
Gábor Timár ()
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Gusztáv Jakab: Department of Environmental and Landscape Geography, Institute of Geography and Earth Science, ELTE Eötvös Loránd University, 1117 Budapest, Hungary
Enikő Magyari: Department of Environmental and Landscape Geography, Institute of Geography and Earth Science, ELTE Eötvös Loránd University, 1117 Budapest, Hungary
Benedek Jakab: RAIZEN.Art, 1113 Budapest, Hungary
Gábor Timár: Department of Geophysics and Space Science, Institute of Geography and Earth Science, ELTE Eötvös Loránd University, 1117 Budapest, Hungary
Land, 2025, vol. 14, issue 7, 1-17
Abstract:
Landscape visualization plays a crucial role in various scientific and artistic fields, including geography, environmental sciences, and digital arts. Recent advancements in computer graphics have enabled more sophisticated approaches to landscape representation. The integration of artificial intelligence (AI) image generation has further improved accessibility for researchers, allowing efficient creation of landscape visualizations. This study presents a comprehensive workflow for the rapid and cost-effective generation of photorealistic still images. The methodology combines AI applications, computational techniques, and photographic methods to reconstruct the historical landscapes of the Great Hungarian Plain, one of Europe’s most significantly altered regions. The most accurate and visually compelling results are achieved by using historical maps and drone imagery as compositional and stylistic references, alongside a suite of AI tools tailored to specific tasks. These high-quality landscape visualizations offer significant potential for scientific research and public communication, providing both aesthetic and informative value. The article, which primarily presents a methodological description, does not contain numerical results. To test the method, we applied a procedure: we ran the algorithm on a current topographic map of a sample area and compared the resulting image with the view model provided by Google Earth.
Keywords: artificial intelligence; drone images; illustrations; landcover change; environmental reconstruction; landscape history (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:7:p:1430-:d:1696756
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