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This paper presents a novel adaptive pattern-driven approach for compressing large-area high-resolution terrain data. Utilizing a pattern-driven model, the proposed approach achieves efficient terrain data reduction by modeling and encoding disparate visual patterns using a compact set of extracted features. The feasibility and efficiency of the proposed technique were corroborated by experiments using various terrain datasets and comparisons with the state-of-the-art compression techniques. Since different visual patterns are separated and modeled explicitly during the compression process, the proposed technique also holds a great potential for providing a good synergy between compression and compressed-domain analysis.