A linear algorithm for incremental digital display of circular arcs
Communications of the ACM
Flow computation on massive grids
Proceedings of the 9th ACM international symposium on Advances in geographic information systems
Efficient Flow Computation on Massive Grid Terrain Datasets
Geoinformatica
TerraStream: from elevation data to watershed hierarchies
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Smugglers and border guards: the GeoStar project at RPI
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
| Hi-index | 0.00 |
We present an error metric based on the potential energy of water flow to evaluate the quality of lossy terrain simplification algorithms. Typically, terrain compression algorithms seek to minimize RMS (root mean square) and maximum error. These metrics fail to capture whether a reconstructed terrain preserves the drainage network. A quantitative measurement of how accurately a drainage network captures the hydrology is important for determining the effectiveness of a terrain simplification technique. Having a measurement for testing and comparing different models has the potential to be widely used in numerous applications (flood prevention, erosion measurement, pollutant propagation, etc). In this paper, we transfer the drainage network computed on reconstructed geometry onto the original uncompressed terrain and use our error metric to measure the level of error created by the simplification. We also present a novel terrain simplification algorithm based on the compression of hydrology features. This method and other terrain compression schemes are then compared using our new metric.