Geo-Aesthetics: An Application-Oriented Generative Framework for Translating Remote Sensing Landscape Morphologies into Parametric Design Patterns

This paper presents Geo-Esthetics, an application-oriented workflow that uses remote sensing imagery as source morphology for generative design. The study addresses a design problem: how can large-scale terrestrial textures be extracted, abstracted, and organized as pattern references for parametric and visual design? Nine representative geomorphological settings were selected. For each case, Sentinel-2 imagery was cropped into a 2 km × 2 km geographic window, enhanced using spectral-index selection and Contrast Limited Adaptive Histogram Equalization (CLAHE), and used as an image prompt in Midjourney v6.0. A consistent prompt structure and parameter setting were applied. Four variants were generated for each case and screened according to topological fidelity, level of abstraction, and design applicability. Box-counting dimension and lacunarity were calculated to compare morphological complexity between source images and generated patterns. The cases show that hydrological, tectonic, desert, agricultural, and reef morphologies can be translated into design-oriented pattern prototypes for paving, façades, interfaces, acoustic elements, and biomimetic surfaces. The contribution of this work lies mainly in design methodology: it provides a documented workflow for connecting Earth observation data, generative AI, and design ideation, while retaining clear boundaries around model reproducibility, prompt sensitivity, case representativeness, and perceptual evaluation.