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2014-08-05T16:35:06+08:00
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2014-08-05T16:35:06+08:00
Acrobat PDFMaker 11 Word 版
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Overcoming Object Misalignment in Geo-spatial Datasets
In integrating geo-spatial datasets, sometimes layers are unable to perfectly overlay each other. In most cases, the cause of misalignment is the cartographic variation of objects forming features in the datasets. Either this could be due to actual changes on ground, collection, or storage approaches used leading to overlapping or openings between features. In this paper, we present an alignment method that uses adjustment algorithms to update the geometry of features within a dataset or complementary adjacent datasets so that they can align to achieve perfect integration. The method identifies every unique spatial instance in datasets and their spatial points that define all their geometry; the differences are compared and used to compute the alignment parameters. This provides a uniform geo-spatial features’ alignment taking into consideration changes in the different datasets being integrated without affecting the topology and attributes.
Ismail Wadembere, Patrick Ogao
Adobe PDF Library 11.0
Object-Based, Geometry Alignment, Geo-Spatial Management
D:20140805083259
In integrating geo-spatial datasets, sometimes layers are unable to perfectly overlay each other. In most cases, the cause of misalignment is the cartographic variation of objects forming features in the datasets. Either this could be due to actual changes on ground, collection, or storage approaches used leading to overlapping or openings between features. In this paper, we present an alignment method that uses adjustment algorithms to update the geometry of features within a dataset or complementary adjacent datasets so that they can align to achieve perfect integration. The method identifies every unique spatial instance in datasets and their spatial points that define all their geometry; the differences are compared and used to compute the alignment parameters. This provides a uniform geo-spatial featuresâ•Ž alignment taking into consideration changes in the different datasets being integrated without affecting the topology and attributes.
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