Tag: SOM
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New paper on the automatic characterization of surface activities from 4D point clouds
An approach for automatic characterization of surface activities from large 4D point clouds is presented in a new paper by Daan Hulskemper et al. in collaboration between the 3DGeo research group and the departments of Geoscience and Remote Sensing and Coastal Engineering at TU Delft. Hulskemper, D., Anders, K., Antolínez, J. A. Á., Kuschnerus, M.,…
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Clustering and Analyzing Air Pollution Data using Self-Organizing Maps
Today Enrico Steiger is presenting the following paper at AGILE 2016 conference Helsinki: Lee, M., Steiger, E. Zipf, A. (2016): Clustering and Analyzing Air Pollution Data using Self-Organizing Maps. 19th AGILE Conference on Geographic Information Science. Helsinki, Finnland. In Geographic Information Science the rise in the availability of spatial data paved ways for increased research…
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New tool for clustering and analyzing spatial data with Neural Networks and Neural Gas
A new tool for clustering and analyzing geographic data with artifical self-organizing neural networks (SOM) and the innovative Neural Gas (NG) algorithms has been made availabe. The free SPAWNN suite supports different spatial context models and it also establishes interactive linkage between the neural network and geographic maps. Notably it enables further the follow-up clustering…