Name
Abstract | ||
|---|---|---|
In order to ease the propagation of updates between geographic datasets of different scales and to support multi-scale analyses, different datasets need to be matched, that is, objects that represent the same entity in the physical world need to be identified. We propose a method for matching datasets of river systems that were acquired at different scales. This task is related to the problem of matching networks of lines, for example road networks. However, we also take into account that rivers may be represented by polygons. The geometric dimension of a river object may depend, for example, on the width of the river and the scale. Our method comprises three steps. First, in order to cope with geometries of different dimensions, we collapse river polygons to centerlines by applying a skeletonization algorithm. We show how to preserve the topology of the river system in this step, which is an important requirement for the subsequent matching steps. Secondly, we perform a pre-matching of the arcs and nodes of the line network generated in the first step, that is, we detect candidate matches and define their quality. Thirdly, we perform the final matching by selecting a consistent set of good candidate matches. We tested our method for two Chinese river datasets of the same areal extent, which were acquired at scales 1:50 000 and 1:250 000. The evaluation of our results allows us to conclude that our method seldom yields incorrect matches. The number of correct matches that are missed by our method is quite small. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-00318-9_7 | ADVANCES IN GISCIENCE |
Keywords | Field | DocType |
data matching,network,multi-scale representation,generalization,skeletonization | Data mining,Polygon,Road networks,Systems engineering,Computer science,Skeletonization | Conference |
ISSN | Citations | PageRank |
1863-2246 | 6 | 0.46 |
References | Authors | |
12 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Birgit Kieler | 1 | 12 | 1.29 |
| Wei Huang | 2 | 6 | 0.80 |
| Jan-Henrik Haunert | 3 | 179 | 19.32 |
| Jie Jiang | 4 | 6 | 0.46 |