Presenter/Author Information

M. Kanevski
A. Pozdnukhov
V. Timonin

Keywords

machine learning algorithms, spatial predictions and mapping, software tool

Start Date

1-7-2008 12:00 AM

Description

Nowadays machine learning (ML), including Artificial Neural Networks (ANN) of different architectures and Support Vector Machines (SVM), provides extremely important tools for intelligent geo- and environmental data analysis, processing and visualisation. Machine learning is an important complement to the traditional techniques like geostatistics. This paper presents a review of several contemporary applications of ML for geospatial data: regional classification of environmental data, mapping of continuous environmental and pollution data, including the use of automatic algorithms, optimization (design/redesign) of monitoring networks.

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Jul 1st, 12:00 AM

Machine Learning Algorithms for GeoSpatial Data. Applications and Software Tools

Nowadays machine learning (ML), including Artificial Neural Networks (ANN) of different architectures and Support Vector Machines (SVM), provides extremely important tools for intelligent geo- and environmental data analysis, processing and visualisation. Machine learning is an important complement to the traditional techniques like geostatistics. This paper presents a review of several contemporary applications of ML for geospatial data: regional classification of environmental data, mapping of continuous environmental and pollution data, including the use of automatic algorithms, optimization (design/redesign) of monitoring networks.