Automatic mapping with prior knowledge in situations of routine and emergency

A hardcopy version including selected papers published online as well as unpublished material written by invited authors has been published in:

Reference: Automatic mapping algorithms for routine and emergency monitoring data. EUR 21595 EN EC. Dubois G. (Ed.), Office for Official Publications of the European Communities, Luxembourg, 150 p., November 2005.

The report can be downloaded at the bottom of this page.

Reviewers and Editorial Committee

• Samy Bengio, Machine Learning Group, Dalle Molle Institute for Perceptual Artificial Intelligence, Switzerland
• Dan Cornford, Neural Computing Research Group, Aston University, United Kingdom
• Gregoire Dubois, Radioactivity Environmental Monitoring. Joint Research Centre, European Commission, Italy
• Stefano Galmarini, Radioactivity Environmental Monitoring, Joint Research Centre, European Commission, Italy
• Pierre Goovaerts, BioMedware Inc., Ann Arbor, Michigan, USA
• Gerard Heuvelink, ALTERRA and Laboratory of Soil Science and Geology, Wageningen University and Research Centre, The Netherlands
• Mikhail Kanevski, Institute of Geomatics and Risk Analysis, University of Lausanne, Switzerland
• Jorgen Pilz, Applied Statistics Group, Institute of Mathematics and Statistics, University of Klagenfurt, Austria

Contents of EUR 21595 EN

Title: Automatic mapping algorithms for routine and emergency monitoring data. Report on the Spatial Interpolation Comparison (SIC2004) exercise

Editors: G. Dubois

Year of publication: 2005

Pages: 150

Reference: EUR 21595 EN

Publisher: Office for Official Publications of the European Communities, Luxembourg

ISBN: 92-894-9400-X

TABLE OF CONTENTS

• Foreword. G. Dubois, p. 1

Introduction

• Spatial Interpolation Comparison (SIC) 2004: introduction to the exercise and overview on the results. G. Dubois and S. Galmarini, p. 7
• Operation of the Dutch 3rd Generation National Radioactivity Monitoring Network. C.J.W. Twenhöfel, C. de Hoog van Beynen, A.P.P.A. van Lunenburg, G.J.E. Slagt, R.B. Tax, P.J.M. van Westerlaak and F.J. Aldenkamp, p. 19

Extended abstracts from the participants

• Ordinary Kriging Abilities for Radioactive Contamination Modelling. E. Savelieva, p. 35
• Mapping radioactivity from monitoring data: automating the classical geostatistical approach. E.J. Pebesma, p. 37
• Automatic Mapping in the Presence of Substitutive Errors: A Robust Kriging Approach. B. Fournier and R. Furrer, p. 39
• Automatic Mapping of Monitoring Data. S. Lophaven, H.B. Nielsen and J. Søndergaard, p. 41
• Bayesian automating fitting functions for spatial predictions. M. Palaseanu-Lovejoy, p. 43
• Fast Spatial Interpolation using Sparse Gaussian Processes. B. Ingram, L. Csató and D. Evans, p. 45
• Interpolation of Radioactivity Data Using Regularized Spline with Tension. J. Hofierka, p. 47
• Automated mapping using multilevel B-Splines. A. Saveliev, A. V. Romanov and S. S. Mukharamova, p. 49
• Spatial interpolation of natural radiation levels with prior information using back-propagation artificial neural networks. J. P Rigol-Sanchez, p. 51
• Spatial Prediction of Radioactivity Using General Regression Neural Network. V. Timonin and E. Savelieva, p. 53
• Investigation of two Neural Network Methods in an Automatic Mapping Exercise. S. Dutta, R. Ganguli and B. Samanta, p. 55
• Support Vector Regression for Automated Robust Spatial Mapping of Natural Radioactivity. A. Pozdnoukhov, p. 57

Discussion papers

• Are comparative studies a waste of time? SIC2004 examined. D. Cornford, p. 61
• The comparison of one click mapping procedures for emergencies. K. G. van den Boogaart, p. 71
• Spatial Interpolation Comparison exercise 2004: a real problem or an academic exercise? D. E. Myers, p. 79
• Automatic Interpolation of Network Data using Indicator Kriging. P. Goovaerts, p. 89
• Identification of Spatial Anisotropy by means of the Covariance Tensor Identity. D. T. Hristopulos, p. 103
• Machine Learning for automatic environmental mapping: when and how? N. Gilardi and S. Bengio, p. 123
• Real-time AI_GEOSTATS for Atmospheric Dispersion Forecasting, and vice versa? S. Galmarini, p. 139

Download EUR 21595 EN

EUR_SIC_2004_online.pdf

Automatic mapping with prior knowledge in situations of routine and emergency

A hardcopy version including selected papers published online as well as unpublished material written by invited authors has been published in:

Reference: Automatic mapping algorithms for routine and emergency monitoring data. EUR 21595 EN EC. Dubois G. (Ed.), Office for Official Publications of the European Communities, Luxembourg, 150 p., November 2005.

The report can be downloaded at the bottom of this page.

Reviewers and Editorial Committee

• Samy Bengio, Machine Learning Group, Dalle Molle Institute for Perceptual Artificial Intelligence, Switzerland
• Dan Cornford, Neural Computing Research Group, Aston University, United Kingdom
• Gregoire Dubois, Radioactivity Environmental Monitoring. Joint Research Centre, European Commission, Italy
• Stefano Galmarini, Radioactivity Environmental Monitoring, Joint Research Centre, European Commission, Italy
• Pierre Goovaerts, BioMedware Inc., Ann Arbor, Michigan, USA
• Gerard Heuvelink, ALTERRA and Laboratory of Soil Science and Geology, Wageningen University and Research Centre, The Netherlands
• Mikhail Kanevski, Institute of Geomatics and Risk Analysis, University of Lausanne, Switzerland
• Jorgen Pilz, Applied Statistics Group, Institute of Mathematics and Statistics, University of Klagenfurt, Austria

Contents of EUR 21595 EN

Title: Automatic mapping algorithms for routine and emergency monitoring data. Report on the Spatial Interpolation Comparison (SIC2004) exercise

Editors: G. Dubois

Year of publication: 2005

Pages: 150

Reference: EUR 21595 EN

Publisher: Office for Official Publications of the European Communities, Luxembourg

ISBN: 92-894-9400-X

TABLE OF CONTENTS

• Foreword. G. Dubois, p. 1

Introduction

• Spatial Interpolation Comparison (SIC) 2004: introduction to the exercise and overview on the results. G. Dubois and S. Galmarini, p. 7
• Operation of the Dutch 3rd Generation National Radioactivity Monitoring Network. C.J.W. Twenhöfel, C. de Hoog van Beynen, A.P.P.A. van Lunenburg, G.J.E. Slagt, R.B. Tax, P.J.M. van Westerlaak and F.J. Aldenkamp, p. 19

Extended abstracts from the participants

• Ordinary Kriging Abilities for Radioactive Contamination Modelling. E. Savelieva, p. 35
• Mapping radioactivity from monitoring data: automating the classical geostatistical approach. E.J. Pebesma, p. 37
• Automatic Mapping in the Presence of Substitutive Errors: A Robust Kriging Approach. B. Fournier and R. Furrer, p. 39
• Automatic Mapping of Monitoring Data. S. Lophaven, H.B. Nielsen and J. Søndergaard, p. 41
• Bayesian automating fitting functions for spatial predictions. M. Palaseanu-Lovejoy, p. 43
• Fast Spatial Interpolation using Sparse Gaussian Processes. B. Ingram, L. Csató and D. Evans, p. 45
• Interpolation of Radioactivity Data Using Regularized Spline with Tension. J. Hofierka, p. 47
• Automated mapping using multilevel B-Splines. A. Saveliev, A. V. Romanov and S. S. Mukharamova, p. 49
• Spatial interpolation of natural radiation levels with prior information using back-propagation artificial neural networks. J. P Rigol-Sanchez, p. 51
• Spatial Prediction of Radioactivity Using General Regression Neural Network. V. Timonin and E. Savelieva, p. 53
• Investigation of two Neural Network Methods in an Automatic Mapping Exercise. S. Dutta, R. Ganguli and B. Samanta, p. 55
• Support Vector Regression for Automated Robust Spatial Mapping of Natural Radioactivity. A. Pozdnoukhov, p. 57

Discussion papers

• Are comparative studies a waste of time? SIC2004 examined. D. Cornford, p. 61
• The comparison of one click mapping procedures for emergencies. K. G. van den Boogaart, p. 71
• Spatial Interpolation Comparison exercise 2004: a real problem or an academic exercise? D. E. Myers, p. 79
• Automatic Interpolation of Network Data using Indicator Kriging. P. Goovaerts, p. 89
• Identification of Spatial Anisotropy by means of the Covariance Tensor Identity. D. T. Hristopulos, p. 103
• Machine Learning for automatic environmental mapping: when and how? N. Gilardi and S. Bengio, p. 123
• Real-time AI_GEOSTATS for Atmospheric Dispersion Forecasting, and vice versa? S. Galmarini, p. 139

Download EUR 21595 EN

EUR_SIC_2004_online.pdf

Automatic mapping with prior knowledge in situations of routine and emergency

A hardcopy version including selected papers published online as well as unpublished material written by invited authors has been published in:

Reference: Automatic mapping algorithms for routine and emergency monitoring data. EUR 21595 EN EC. Dubois G. (Ed.), Office for Official Publications of the European Communities, Luxembourg, 150 p., November 2005.

The report can be downloaded at the bottom of this page.

Reviewers and Editorial Committee

• Samy Bengio, Machine Learning Group, Dalle Molle Institute for Perceptual Artificial Intelligence, Switzerland
• Dan Cornford, Neural Computing Research Group, Aston University, United Kingdom
• Gregoire Dubois, Radioactivity Environmental Monitoring. Joint Research Centre, European Commission, Italy
• Stefano Galmarini, Radioactivity Environmental Monitoring, Joint Research Centre, European Commission, Italy
• Pierre Goovaerts, BioMedware Inc., Ann Arbor, Michigan, USA
• Gerard Heuvelink, ALTERRA and Laboratory of Soil Science and Geology, Wageningen University and Research Centre, The Netherlands
• Mikhail Kanevski, Institute of Geomatics and Risk Analysis, University of Lausanne, Switzerland
• Jorgen Pilz, Applied Statistics Group, Institute of Mathematics and Statistics, University of Klagenfurt, Austria

Contents of EUR 21595 EN

Title: Automatic mapping algorithms for routine and emergency monitoring data. Report on the Spatial Interpolation Comparison (SIC2004) exercise

Editors: G. Dubois

Year of publication: 2005

Pages: 150

Reference: EUR 21595 EN

Publisher: Office for Official Publications of the European Communities, Luxembourg

ISBN: 92-894-9400-X

Automatic mapping with prior knowledge in situations of routine and emergency

A hardcopy version including selected papers published online as well as unpublished material written by invited authors has been published in:

Reference: Automatic mapping algorithms for routine and emergency monitoring data. EUR 21595 EN EC. Dubois G. (Ed.), Office for Official Publications of the European Communities, Luxembourg, 150 p., November 2005.

The report can be downloaded at the bottom of this page.

Reviewers and Editorial Committee

• Samy Bengio, Machine Learning Group, Dalle Molle Institute for Perceptual Artificial Intelligence, Switzerland
• Dan Cornford, Neural Computing Research Group, Aston University, United Kingdom
• Gregoire Dubois, Radioactivity Environmental Monitoring. Joint Research Centre, European Commission, Italy
• Stefano Galmarini, Radioactivity Environmental Monitoring, Joint Research Centre, European Commission, Italy
• Pierre Goovaerts, BioMedware Inc., Ann Arbor, Michigan, USA
• Gerard Heuvelink, ALTERRA and Laboratory of Soil Science and Geology, Wageningen University and Research Centre, The Netherlands
• Mikhail Kanevski, Institute of Geomatics and Risk Analysis, University of Lausanne, Switzerland
• Jorgen Pilz, Applied Statistics Group, Institute of Mathematics and Statistics, University of Klagenfurt, Austria

Contents of EUR 21595 EN

Title: Automatic mapping algorithms for routine and emergency monitoring data. Report on the Spatial Interpolation Comparison (SIC2004) exercise

Editors: G. Dubois

Year of publication: 2005

Pages: 150

Reference: EUR 21595 EN

Publisher: Office for Official Publications of the European Communities, Luxembourg

ISBN: 92-894-9400-X

TABLE OF CONTENTS

• Foreword. G. Dubois, p. 1

Introduction

• Spatial Interpolation Comparison (SIC) 2004: introduction to the exercise and overview on the results. G. Dubois and S. Galmarini, p. 7
• Operation of the Dutch 3rd Generation National Radioactivity Monitoring Network. C.J.W. Twenh?fel, C. de Hoog van Beynen, A.P.P.A. van Lunenburg, G.J.E. Slagt, R.B. Tax, P.J.M. van Westerlaak and F.J. Aldenkamp, p. 19

Extended abstracts from the participants

• Ordinary Kriging Abilities for Radioactive Contamination Modelling. E. Savelieva, p. 35
• Mapping radioactivity from monitoring data: automating the classical geostatistical approach. E.J. Pebesma, p. 37
• Automatic Mapping in the Presence of Substitutive Errors: A Robust Kriging Approach. B. Fournier and R. Furrer, p. 39
• Automatic Mapping of Monitoring Data. S. Lophaven, H.B. Nielsen and J. S?ndergaard, p. 41
• Bayesian automating fitting functions for spatial predictions. M. Palaseanu-Lovejoy, p. 43
• Fast Spatial Interpolation using Sparse Gaussian Processes. B. Ingram, L. Csat? and D. Evans, p. 45
• Interpolation of Radioactivity Data Using Regularized Spline with Tension. J. Hofierka, p. 47
• Automated mapping using multilevel B-Splines. A. Saveliev, A. V. Romanov and S. S. Mukharamova, p. 49
• Spatial interpolation of natural radiation levels with prior information using back-propagation artificial neural networks. J. P Rigol-Sanchez, p. 51
• Spatial Prediction of Radioactivity Using General Regression Neural Network. V. Timonin and E. Savelieva, p. 53
• Investigation of two Neural Network Methods in an Automatic Mapping Exercise. S. Dutta, R. Ganguli and B. Samanta, p. 55
• Support Vector Regression for Automated Robust Spatial Mapping of Natural Radioactivity. A. Pozdnoukhov, p. 57

Discussion papers

• Are comparative studies a waste of time? SIC2004 examined. D. Cornford, p. 61
• The comparison of one click mapping procedures for emergencies. K. G. van den Boogaart, p. 71
• Spatial Interpolation Comparison exercise 2004: a real problem or an academic exercise? D. E. Myers, p. 79
• Automatic Interpolation of Network Data using Indicator Kriging. P. Goovaerts, p. 89
• Identification of Spatial Anisotropy by means of the Covariance Tensor Identity. D. T. Hristopulos, p. 103
• Machine Learning for automatic environmental mapping: when and how? N. Gilardi and S. Bengio, p. 123

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"Automatic mapping with prior knowledge in situations of routine and emergency"

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Applied GIS (AGIS) published the accepted papers in a special issue (Vol. 1, No. 2). See http://publications.epress.monash.edu/loi/ag/index.html

A hardcopy version including selected papers published online as well as unpublished material written by invited authors has been published in:

Reference: Automatic mapping algorithms for routine and emergency monitoring data. EUR 21595 EN EC. Dubois G. (Ed.), Office for Official Publications of the European Communities, Luxembourg, 150 p., November 2005.

The report can be downloaded at the bottom of this page.

<-- Text: [end] -->

<-- Header: [begin] -->

Reviewers and Editorial Committee

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• Samy Bengio, Machine Learning Group, Dalle Molle Institute for Perceptual Artificial Intelligence, Switzerland
• Dan Cornford, Neural Computing Research Group, Aston University, United Kingdom
• Gr?goire Dubois, Radioactivity Environmental Monitoring. Joint Research Centre, European Commission, Italy
• Stefano Galmarini, Radioactivity Environmental Monitoring, Joint Research Centre, European Commission, Italy
• Pierre Goovaerts, BioMedware Inc., Ann Arbor, Michigan, USA
• Gerard Heuvelink, ALTERRA and Laboratory of Soil Science and Geology, Wageningen University and Research Centre, The Netherlands
• Mikhail Kanevski, Institute of Geomatics and Risk Analysis, University of Lausanne, Switzerland
• J?rgen Pilz, Applied Statistics Group, Institute of Mathematics and Statistics, University of Klagenfurt, Austria

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Contents of EUR 21595 EN

<-- Header: [end] -->

<-- Text: [begin] -->

Title: Automatic mapping algorithms for routine and emergency monitoring data. Report on the Spatial Interpolation Comparison (SIC2004) exercise

Editors: G. Dubois

Year of publication: 2005

Pages: 150

Reference: EUR 21595 EN

Publisher: Office for Official Publications of the European Communities, Luxembourg

ISBN: 92-894-9400-X

TABLE OF CONTENTS

• Foreword. G. Dubois, p. 1

Introduction

• Spatial Interpolation Comparison (SIC) 2004: introduction to the exercise and overview on the results. G. Dubois and S. Galmarini, p. 7
• Operation of the Dutch 3rd Generation National Radioactivity Monitoring Network. C.J.W. Twenh?fel, C. de Hoog van Beynen, A.P.P.A. van Lunenburg, G.J.E. Slagt, R.B. Tax, P.J.M. van Westerlaak and F.J. Aldenkamp, p. 19

Extended abstracts from the participants

• Ordinary Kriging Abilities for Radioactive Contamination Modelling. E. Savelieva, p. 35
• Mapping radioactivity from monitoring data: automating the classical geostatistical approach. E.J. Pebesma, p. 37
• Automatic Mapping in the Presence of Substitutive Errors: A Robust Kriging Approach. B. Fournier and R. Furrer, p. 39
• Automatic Mapping of Monitoring Data. S. Lophaven, H.B. Nielsen and J. S?ndergaard, p. 41
• Bayesian automating fitting functions for spatial predictions. M. Palaseanu-Lovejoy, p. 43
• Fast Spatial Interpolation using Sparse Gaussian Processes. B. Ingram, L. Csat? and D. Evans, p. 45
• Interpolation of Radioactivity Data Using Regularized Spline with Tension. J. Hofierka, p. 47
• Automated mapping using multilevel B-Splines. A. Saveliev, A. V. Romanov and S. S. Mukharamova, p. 49
• Spatial interpolation of natural radiation levels with prior information using back-propagation artificial neural networks. J. P Rigol-Sanchez, p. 51
• Spatial Prediction of Radioactivity Using General Regression Neural Network. V. Timonin and E. Savelieva, p. 53
• Investigation of two Neural Network Methods in an Automatic Mapping Exercise. S. Dutta, R. Ganguli and B. Samanta, p. 55
• Support Vector Regression for Automated Robust Spatial Mapping of Natural Radioactivity. A. Pozdnoukhov, p. 57

Discussion papers

• Are comparative studies a waste of time? SIC2004 examined. D. Cornford, p. 61
• The comparison of one click mapping procedures for emergencies. K. G. van den Boogaart, p. 71
• Spatial Interpolation Comparison exercise 2004: a real problem or an academic exercise? D. E. Myers, p. 79
• Automatic Interpolation of Network Data using Indicator Kriging. P. Goovaerts, p. 89
• Identification of Spatial Anisotropy by means of the Covariance Tensor Identity. D. T. Hristopulos, p. 103
• Machine Learning for automatic environmental mapping: when and how? N. Gilardi and S. Bengio, p. 123
• Real-time Geostatistics for Atmospheric Dispersion Forecasting, and vice versa? S. Galmarini, p. 139

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Download EUR 21595 EN

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