Conflation Of Geospatial Vector Data From Sea Chart And Topographic Map

The gathering of geospatial data is very important in GIS applications. The same spatial data is sometimes colledted by different departments, which will cause the waste of human and financial resources, and brings data ambiguity. These problems bring many difficulties to data share and data integration between different GIS departments. An effective way to solve this problem is the geospatial data conflation technique. The 21th century is the ocean's century. To satisfy the need of coastal economy development and coastal city information, conflation of geospatial vector data from a sea chart and a topographic map is studied in this paper. It solves the differences in coordinate systems, projections, geometries, codings etc and uncertainty of conflating results.General process of geospatial data conflation from multi-sources is summaried. Then the problems to be solved before putting geospatial data conflation into practice are discussed. And the process of uniting coordinate systems and projections of a sea chart and a topographic map is presented. These are the preconduction of the research on geospatial data conflation from multi-sources.Disparities of features that represent the same real world entity from different sources usually occur, thus their identification or matching is crutial to map conflation. Based on the spatial similarity theory and motivated by the idea of identifying the same entity through integrating known information by eyes, an entity matching algorithm is proposed in this paper. Regarding the entity as a whole, the total similarity is obtained by integrating positional similarity, shape similarity etc with a weighted average algorithm. And the weights are obtained based on vision theory and the characters of identifying graphics by eyes. Then the matching entities are obtained according to the maximum total similarity. Test results are consistent with human intuition, which show the stability and reliability of the proposed algorithm. Compared with other algorithms, precision and recall of the proposed algorithm are obviously improved. This is the base of solving the geometry conflation.Based on the matching of same entity, in order to solve their conflict and to synthesize the influence of elements precision on different source maps, an element adjusting algorithm based on multi-evaluation factors is proposed. Three primary evaluation factors are analyzed, and the element reliability is gained by integrating the three factors. Then the adjusted position is obtained with a weighted average algorithm. Some same elements from a sea chart and a topographic map are utilized to test the proposed algorithm. The result shows that the quality of areal element adjusting is improved. This is crucial in solving the geometry conflation.Coding conflation is also studied in this paper. Based on the explanation of principles and methods of element classification and coding, the principles and steps of conflated element classification and coding are presented. Guided by these principles, the differences of element coding from a sea chart and a topographic map are analyzed. Some key problems such as element layer transformation and the same element coding uniting are solved, and the coflation of coding is realized.The aim of geospatial data conflation is to improve the information content after conflation. The decrease of information uncertainty means the incerease of information content. Therefore, in the final chapter, the uncertainty of the conflation results is analyzed. The reasons that causing vector data uncertainty and the law of spreading uncertainty are introduced. Then the model of data uncertainty from single source is founded. And the relation of uncertainty from single source and from conflated results is constructed accoding to the unite model of uncertainty from multi-sources. This can be used to evaluate the quality of geospatial data conflation.