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Auteur Fen Luo
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Automatische Interpretation von Semantik aus digitalen Karten im World Wide Web / Fen Luo (2014)
Titre : Automatische Interpretation von Semantik aus digitalen Karten im World Wide Web Type de document : Thèse/HDR Auteurs : Fen Luo, Auteur ; Dieter Fritsch, Directeur de thèse ; Ralf Bill, Directeur de thèse Editeur : Stuttgart : Institüt für Photogrammetrie der Universität Stuttgart Année de publication : 2014 Note générale : bibliographie
Von der Fakultät Luft- und Raumfahrttechnik und Geodäsie der Universität Stuttgart zur Erlangung der Würde eines Doktors der Ingenieurwissenschaften (Dr.-Ing.) genehmigte Abhandlung
Langues : Allemand (ger) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes descripteurs IGN] carte de Kohonen
[Termes descripteurs IGN] données maillées
[Termes descripteurs IGN] données vectorielles
[Termes descripteurs IGN] échelle cartographique
[Termes descripteurs IGN] exploration de données géographiques
[Termes descripteurs IGN] objet géographique
[Termes descripteurs IGN] réseau neuronal artificiel
[Termes descripteurs IGN] toile d'araignée mondiale
Résumé : (auteur) On the Internet there are innumerable spatial data representing different sections of the world in form of raster and vector maps. The information contained in these maps is not automatically discoverable, since it is encoded by means of certain map elements. Its semantics is not explicit unless interpreted by an observer. However, the map information can be interpreted by not only humans but also ma-chines. This already requires the large amount of data to be interpreted. We are going to summarize the automatic derivation of semantics from the maps in terms of automatic map interpretation. It involves a process of making the implicit information of a map inventory explicit. For this purpose we present the map interpretation as solutions.
The map interpretation of the current study is done with vector maps what can be found on the internet. For the targeted search of vector maps of the internet, a web crawler is specially developed. The web crawler is a search engine that specifically looks for vector maps. For this, exclusively the shapefile format is sought, which has become a standard format in the GIS environment and in which the vector maps are usually stored. In order to find shapefiles as many as possible, the search is carried out on servers where the probability of finding shapefiles is high. These servers were previously found through the keyword “shapefile download” by Google search.
The maps interpretation includes methods of interpretation of the map objects, of the map types, and of the map scale. First, we will introduce the method of interpreting the map objects. Our aim is to automatically detect the objects based on their specific characteristics. The object recognition is based on self-organizing map (SOM) that is borrowed from artificial intelligence. The map objects are clas-sified into, for example, building floor plan and road network. Its own characteristics should be found for each class and brought in one of the accessible forms of SOM, in this case, a parameter vector. The parameter vectors form the input patterns that are learned in the training phase of SOM. After the input patterns of all object classes of SOM have been learned, the parameter vector is evaluated for each of the present objects on the map and given to the SOM. By the previously successful learning of the input pattern, the objects can be assigned based on each of their calculated parameter vectors of the corresponding object class.
The interpretation of map type is presented as another method. Maps are categorized into different types according to their substantive content and purpose, such as river maps, road maps, contour maps, etc. As for the interpretation of objects, SOM is used here. Hence the input patterns will also be learned which represent the geometric characteristics of the map types. The characteristics arise from both the structure of individual objects and the topology between objects on a map. Now, with a given map in the SOM, the SOM recognizes the appropriate map type according to the learned input pattern. In addition, one obtains the filenames of the maps as well as the content of the website where the map was found. In the present thesis we also investigated how this additional information can help in the interpretation of map type.
The automatic interpretation of the map scale is a further method in addition to the interpretation of the map objects and map types, which is discussed in the present thesis. The interpretation of the map scale is implemented in two ways: the multi-representation and the details grade. In the former case, the scale of the relevant representation can be derived, where an identical object in different realistic representations on the map is shown; while in the latter case, the scale is derived from the details grade, on the basis of the fact that maps with different scales are displayed on different levels of details.
Numéro de notice : 17347 Thématique : GEOMATIQUE Nature : Thèse étrangère En ligne : http://dx.doi.org/10.18419/opus-3960 Format de la ressource électronique : URL Permalink :
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