Incremental semi-automatic correction of misclassified spatial objects

Authors: Prossegger, M. and Bouchachia, A.

Journal: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume: 6943 LNAI

Pages: 16-25

eISSN: 1611-3349

ISBN: 9783642238567

ISSN: 0302-9743

DOI: 10.1007/978-3-642-23857-4_6

Abstract:

This paper proposes a decision tree based approach for semi-automatic correction of misclassified spatial objects in the Austrian digital cadastre map. Departing from representative areas, proven to be free of classification errors, an incremental decision tree is constructed. This tree is used later to identify and correct misclassified spatial objects. The approach is semiautomatic due to the interaction with the user in case of inaccurate assignments. During the learning process, whenever new (training) spatial data becomes available, the decision tree is then incrementally adapted without the need to generate a new tree from scratch. The approach has been evaluated on a large and representative area from the Austrian digital cadastre map showing a substantial benefit. © 2011 Springer-Verlag.

Source: Scopus

Preferred by: Hamid Bouchachia