Machine Learning Algorithms for Analysis and Modeling of GeoSpatial Data

摘要：

The paper presents an overview of the recently developed approaches and results concerning the application of Machine Learning algorithms for environmental data analysis and modelling problems. These include all the main tasks of supervised and unsupervised learning from spatio-temporal data. The incorporation of additional information into spatial models, such as secondary variables and/or co-ordinates, digital elevation models and CIS information is considered. The real case studies which are mentioned in the paper deal with the environmental problems of pollution data modelling and topo-climatic mapping.

作者: Alexei Pozdnoukhov Mikhail Kanevski

作者单位: Institute of Geomatics and Analysis of Risk, University of Lausanne, Switzerland

会议类型: 国际会议

会议名称: The 12th Conference of the International Association for Mathematical Geology(第12届国际数学地质大会)

会议地点: 北京

会议语种:英文

页码: 216-219

在线出版日期: 2007-08-26（万方平台首次上网日期，不代表论文的发表时间）