Work

Machine Learning Algorithms for GeoSpatial Data. Applications and Software Tools

Year: 2008

Type: article

Abstract: 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.

Source: ScholarsArchive (Brigham Young University)

Authors: Mikhaïl Kanevski, A. Pozdnukhov, V. Timonin

Institution: University of Lausanne

Language: English

Topic: Soil Geostatistics and Mapping

Subfield: Soil Science

Field: Agricultural and Biological Sciences

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Awards: 'GeoKernels': Kernel-Based Methods for Geo- and Environmental Sciences, EXAFS and zeolites in heterogeneous catalysis, Urbanization regime and environmental impact: Analysis and Modelling of Urban Patterns, Clustering and Metamorphoses, G993971353

OpenAlex ID: W2587088492

FWCI: 0.928

Cites: 5

Cited by: 26

Related to: 10