<p>This study proposes an informatics-driven geospatial framework for extracting, classifying, and quantitatively analysing terrestrial surface patterns in Eastern India using high-resolution satellite observations. Multi-spectral data from QuickBird satellites, obtained via the European Space Agency, were systematically pre-processed to derive robust geophysical parameters. The workflow integrated advanced geospatial intelligence methodologies for surface feature delineation, including Gaussian noise filtering, Contrast enhancement, and Adaptive median filtering to optimise spectral fidelity and suppress sensor noise. False-colour composites constructed from multispectral bands facilitated the accurate classification of diverse land cover categories, including vegetation, hydrological features, and urban infrastructure. Georeferencing and on-screen digitisation enabled the generation of detailed thematic layers, integrated into a unified geospatial information. The framework significantly enhances the accuracy, reproducibility, and scalability of surface pattern analysis, offering a robust tool for sustainable land resource management and environmental monitoring at regional scales.</p>