Principles of automated learning in a GIS using an unlimited set of object-oriented persistent neurons

Francois Bouille

doi:10.1117/12.182857

17 August 1994 Principles of automated learning in a GIS using an unlimited set of object-oriented persistent neurons

Francois Bouille

Proceedings Volume 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision; (1994) https://doi.org/10.1117/12.182857
Event: Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, 1994, Munich, Germany

Abstract

HBDS offers a class-based integrated platform which is a kernel for GIS, in which anything is modeled by using persistent abstract data types, possibly in a fuzzy context. One of the most interesting and efficient concepts is the neuron; in HBDS, a neuron is an object of a class of neurons, this one being included in the hyperclass `neuron,' and thus inheriting an algorithmic `behavior' which is automatically activated when one of its `launchers,' attributes or links, is invoked by an external agent; then the neuron may perform actions on other components. This neuron is able to take into account its preceding experiences owing to some algorithms included in the neural engine. The neural models are particularly easy to design and develop, and represent an up-to-date methodology very promising in GIS. They nevertheless require a special training and education for new geographers as well as for those still active.

Citation Download Citation

Francois Bouille "Principles of automated learning in a GIS using an unlimited set of object-oriented persistent neurons", Proc. SPIE 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, (17 August 1994); https://doi.org/10.1117/12.182857

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