Terrain modelling with B-spline type surfaces defined on curved knot lines
Image and Vision Computing
Real-time, continuous level of detail rendering of height fields
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
ROAMing terrain: real-time optimally adapting meshes
VIS '97 Proceedings of the 8th conference on Visualization '97
Contour interpolation and surface reconstruction of smooth terrain models
Proceedings of the conference on Visualization '98
Making large-scale support vector machine learning practical
Advances in kernel methods
Proceedings of the 28th annual conference on Computer graphics and interactive techniques
Support vector machine active learning for image retrieval
MULTIMEDIA '01 Proceedings of the ninth ACM international conference on Multimedia
Digital Image Processing
Terrain Simplification Simplified: A General Framework for View-Dependent Out-of-Core Visualization
IEEE Transactions on Visualization and Computer Graphics
Constructing a dem from grid-based data by computing intermediate contours
GIS '03 Proceedings of the 11th ACM international symposium on Advances in geographic information systems
A NOVEL contour-based 3D terrain matching algorithm using wavelet transform
Pattern Recognition Letters
Terrain generation using genetic algorithms
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Region-based artificial terrain texture generation
ICVR'07 Proceedings of the 2nd international conference on Virtual reality
Sentient world: human-based procedural cartography
EvoMUSART'13 Proceedings of the Second international conference on Evolutionary and Biologically Inspired Music, Sound, Art and Design
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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.