Matching of Datasets
نویسنده
چکیده
A multiple representation database contains several geographic datasets defined at different resolutions covering the same area. Objects within the different datasets that represent the same real world feature are connected. This paper presents a method to build a multiple representation database by automatically creating these connections. First an analysis is made of how cartographic generalisation changes object representation and the impact this has on automated matching. Based on this discussion a method for automated matching is proposed. The matching process starts with an object in the small scale dataset and selects a set of possible matches in the large scale dataset. To find the best match, different measures based on semantics, geometry, topology and inter-object relationships are used. These measures form an n-dimensional space and the object(s) in the large scale dataset that is closest to the current small scale object is considered to be the best match. The different measures are given different importance using weights acquired by analysing a manually matched multiple representation database.
منابع مشابه
Matching of Polygon Objects by Optimizing Geometric Criteria
Despite the semantic criteria, geometric criteria have different performances on polygon feature matching in different vector datasets. By using these criteria for measuring the similarity of two polygons in all matchings, the same results would not have been obtained. To achieve the best matching results, the determination of optimal geometric criteria for each dataset is considered necessary....
متن کاملEvaluation of Stereo Matching Costs on Close Range, Aerial and Satellite Images
In the last years, most dense stereo matching methods use evaluation on the Middlebury stereo vision benchmark datasets. Most recent stereo algorithms were designed to perform well on these close range stereo datasets with relatively small baselines and good radiometric behaviour. In this paper, different matching costs on the Semi-Global Matching algorithm are evaluated and compared using the ...
متن کاملSample-oriented Domain Adaptation for Image Classification
Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. The conventional image processing algorithms cannot perform well in scenarios where the training images (source domain) that are used to learn the model have a different distribution with test images (target domain). Also, many real world applicat...
متن کاملA Generalized Joint Inference Approach for Citation Matching
Citation matching is the problem of extracting bibliographic records from citation lists in technical papers, and merging records that represent the same publication. Generally, there are three types of datasets in citation matching, i.e., sparse, dense and hybrid types. Typical approaches for citation matching are Joint Segmentation (Jnt-Seg) and Joint Segmentation Entity Resolution (Jnt-Seg-E...
متن کاملAutomatically and Accurately Matching Objects in Geospatial Datasets
Identification of the same object represented in diverse geospatial datasets is a fundamental problem in spatial data handling and a variety of its applications. This need is becoming increasingly important as extraordinary amounts of geospatial data are collected and shared every day. Numerous difficulties exist in gathering information about objects of interest from diverse datasets, includin...
متن کاملQuality Inspection and Quality Improvement of Large Spatial Datasets
In this paper we present an approach for quality inspection and quality improvement of spatial data that is based on map matching and map fusion. Two different datasets (GDF/TeleAtlas and OpenStreetMap) are used in this study. At first, the edges in the two datasets are matched manually with a tool developed in VBA and ArcGIS. Then, the form of the matching pairs in both datasets is calculated ...
متن کامل