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Tag Archive 'SOM'

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, [...]

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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 in [...]

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The Special Issue: Human Dynamics in the Mobile and Big Data Era of the International Journal of Geographical Information Science (IJGIS), Volume 30, Issue 9 is now available online on Taylor & Francis Online.
This issue includes our article:
Exploration of spatiotemporal and semantic clusters of Twitter data using unsupervised neural networks
Enrico Steiger, Bernd Resch [...]

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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 of [...]

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