We examine a smugglers and border guards scenario. We place observers on a terrain so as to optimize their
visible coverage area. Then we compute a path that a smuggler would take so as to avoid detection, while also
minimizing the path length. We also examine how our results are affected by using a lossy representation of the
We propose three new application-specific error metrics for evaluating terrain compression. Our target terrain
applications are the optimal placement of observers on a landscape and the navigation through the terrain by
smugglers. Instead of using standard metrics such as average or maximum elevation error, we seek to optimize
our compression on the specific real-world application of smugglers and border guards.
We describe a surface compression technique to lossily compress elevation datasets. Our approach first approximates
the uncompressed terrain using an over-determined system of linear equations based on the Laplacian
partial differential equation. Then the approximation is refined with respect to the uncompressed terrain using
an error metric. These two steps work alternately until we find an approximation that is good enough. We
then further compress the result to achieve a better overall compression ratio. We present experiments and
measurements using different metrics and our method gives convincing results.
We propose a novel compression scheme to achieve lossy compression of elevation datasets. Our scheme does not use
predictors in the traditional sense. Our predictors are based on planar dataset segments. We believe that this is a far
better way of expressing context in an elevation dataset since it can capture continuities in different geometries and
allows us to provide an error bound on the output.