Incremental semi-automatic correction of misclassified spatial objects

Authors:
Markus Prossegger;Abdelhamid Bouchachia
Affiliations:
Carinthia University of Applied Sciences, School of Network Engineering and Communication, Klagenfurt, Austria;Alpen Adria University, Department of Informatics, Klagenfurt, Austria
Venue:
ICAIS'11 Proceedings of the Second international conference on Adaptive and intelligent systems
Year:
2011

Citing 6
Cited 0

Information quality benchmarks: product and service performance

Communications of the ACM - Supporting community and building social capital
Data quality assessment

Communications of the ACM - Supporting community and building social capital
Incremental Induction of Decision Trees

Machine Learning
Topological Error Correcting in GIS

SSD '97 Proceedings of the 5th International Symposium on Advances in Spatial Databases
Integrity constraints in spatial databases

Database integrity
Beyond accuracy: what data quality means to data consumers

Journal of Management Information Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper proposes a decision tree based approach for semiautomatic 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.