Example-based realistic terrain generation

In this paper, a new approach to terrain generation based on terrain examples is proposed. Existing procedural algorithms for generation of terrain have several shortcomings. The most popular approach, fractal-based terrain generation, is efficient, but is difficult for users to control. In this paper, we provide a semiautomatic method of terrain generation that uses a four-process genetic algorithm approach to produce a variety of terrain types using only intuitive user inputs. We allow users to specify a rough sketch of terrain silhouette map, retrieve terrain examples based on support vector machine (SVM) from the terrain dataset, cut a region from the terrain examples and fill in the terrain silhouette map. We also generate a photorealistic texture based on the aerial or satellite images. Consequently, we generate the terrain which has both geometrical data and texture data and provide a balance between user input and real-world data capture unmatched.