Data representation in neural networks
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Journal of Mathematical Imaging and Vision
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
Pattern recognition using neural networks: theory and algorithms for engineers and scientists
An efficient algorithm for finding the CSG representation of a simple polygon
SIGGRAPH '88 Proceedings of the 15th annual conference on Computer graphics and interactive techniques
Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks
Solving Problems in Environmental Engineering and Geosciences with Artificial Neural Networks
Artificial Intelligence in Geography
Artificial Intelligence in Geography
Point-in-Polygon Analysis Under Certainty and Uncertainty
Geoinformatica
Uncertainty Management for Spatial Data in Databases: Fuzzy Spatial Data Types
SSD '99 Proceedings of the 6th International Symposium on Advances in Spatial Databases
People Manipulate Objects (but Cultivate Fields): Beyond the Raster-Vector Debate in GIS
Proceedings of the International Conference GIS - From Space to Territory: Theories and Methods of Spatio-Temporal Reasoning on Theories and Methods of Spatio-Temporal Reasoning in Geographic Space
GIS: A Computing Perspective, 2nd Edition
GIS: A Computing Perspective, 2nd Edition
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The degree of uncertainty of many geographical objects has long been known to be in intimate relation with the scale of its observation and representation. Yet, the explicit consideration of scaling operations when modeling uncertainty is rarely found. In this study, a neural network-based data model was investigated for representing geographical objects with scale-induced indeterminate boundaries. Two types of neural units, combined with two types of activation function, comprise the processing core of the model, where the activation function can model either hard or soft transition zones. The construction of complex fuzzy regions, as well as lines and points, is discussed and illustrated with examples. It is shown how the level of detail that is apparent in the boundary at a given scale can be controlled through the degree of smoothness of each activation function. Several issues about the practical implementation of the model are discussed and indications on how to perform complex overlay operations of fuzzy maps provided. The model was illustrated through an example of representing multi-resolution, sub-pixel maps that are typically derived from remote sensing techniques.