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    Statistical language models for topographic data recognition

    Winstanley, Adam C. and Salaik, B. and Keyes, Laura (2003) Statistical language models for topographic data recognition. In: 2003 IEEE International Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. IEEE, pp. 1808-1810. ISBN 0780379292

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    The success of Statistical Language Models (SLMs) at improving the performance of Natural Language Processing (NLP) applications suggests their possible applicability to the area of automated map reading. This idea stems from the fact that there are similarities between natural language and cartographic language. We describe a method of using SLM to characterise the context of different classes of objects. We use these models to measure the frequency of each feature context. This can be used to help identify unclassified map features in combination with other methods (for example, based on an object’s shape).

    Item Type: Book Section
    Keywords: Statistical language models; Natural language processing; topographic data recognition; automated map reading;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8106
    Identification Number:
    Depositing User: Dr. Adam Winstanley
    Date Deposited: 30 Mar 2017 14:17
    Publisher: IEEE
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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