Table Of ContentStudies in Computational Intelligence 839
Shrutilipi Bhattacharjee
Soumya Kanti Ghosh
Jia Chen
Semantic
Kriging for
Spatio-temporal
Prediction
Studies in Computational Intelligence
Volume 839
Series Editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
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Shrutilipi Bhattacharjee
(cid:129)
Soumya Kanti Ghosh Jia Chen
(cid:129)
Semantic Kriging
for Spatio-temporal
Prediction
123
Shrutilipi Bhattacharjee SoumyaKanti Ghosh
Department ofElectrical andComputer Department ofComputer Science
Engineering andEngineering
Technical University of Munich (TUM) Indian Institute of Technology
Munich,Bayern, Germany Kharagpur, West Bengal, India
Jia Chen
Department ofElectrical andComputer
Engineering
Technical University of Munich (TUM)
Munich,Bayern, Germany
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-981-13-8663-3 ISBN978-981-13-8664-0 (eBook)
https://doi.org/10.1007/978-981-13-8664-0
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Dedicated to all young geospatial scientists
Preface
Modeling climatological dynamics and weather patterns have been studied exten-
sively in remote sensing (RS) and geographic information system (GIS). These
analysesarecrucialforvariousgeospatialapplications,suchasclimatologicaltrend
analysis, prediction and forecasting, urban growth modeling. The meteorological
parameters, closely related to earth surface, play important roles for any climato-
logical study. Prediction of these parameters is one of the crucial preprocessing
steps involved in most of the analyses. The geostatistical spatial interpolation
methodsarereportedtobethemostefficientchoiceforpredictingthoseparameters
which are derived from the satellite (raster) imagery. These methods facilitate
improved modeling of spatial autocorrelation/proximity, hence producing minimal
error.Itisalsoobservedthattheinterdependenciesbetweenthemeteorologicaland
terrestrial dynamics play a critical role in the proximity estimation. The semantic
modeling of these land–atmosphere interactions and analyzing the associations
between different factors are obvious for the betterment in the prediction process.
Thisbookfocusesonthesemanticland–atmosphereinteractionmodelingforthe
meteorological parameters that are correlated and influenced by the terrestrial
dynamics. A new spatial interpolation method is presented, namely semantic
kriging (SemK), which is capable not only to model the terrestrial
land-use/land-cover (LULC) distribution, but also to incorporate this property into
the existing interpolation method to make the prediction process more pragmatic
andaccurate. Itisanovelapproachtoextendanyspatialinterpolationmethod(for
meteorological parameters) with contextual/semantic LULC knowledge of the ter-
rain. A hierarchical ontology-based approach has been adopted to quantify the
same. To blend this semantic knowledge into the interpolation process, the most
popular interpolation method reported in the literature, i.e., ordinary kriging (OK),
vii
viii Preface
has been extended further in the semantic domain. The SemK is categorized as a
geostatistical univariate spatial interpolation method, which aims to minimize the
variance of estimation error.
Munich, Germany Shrutilipi Bhattacharjee
Kharagpur, India Soumya Kanti Ghosh
Munich, Germany Jia Chen
December 2018
Acknowledgements
This work is mainly carried out under the INSPIRE Fellowship Scheme by the
Department of Science and Technology (DST), New Delhi, India. The doctoral
curricular of the first author is supported by this fellowship. It is also partially
funded by the German Excellence Initiative and the European Union Seventh
FrameworkProgramundergrantagreementn°291763.Currently,boththefirstand
third authors are supported by this fellowship.
We are indebted to Prof. Pabitra Mitra from the Department of Computer
Science and Engineering, Indian Institute of Technology Kharagpur, India, and
Prof. Shashi Shekhar from the Department of Computer Science, University of
Minnesota, USA, for their valuable comments and suggestions to improve this
work.
We would like to thank the staff at Springer, in particular Mr. Aninda Bose for
technical help and support.
Munich, Germany Shrutilipi Bhattacharjee
Kharagpur, India Soumya Kanti Ghosh
Munich, Germany Jia Chen
ix
Contents
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Overview of Spatial Interpolation . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Research Issues and Challenges in Remote Sensing based
Prediction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Specifications of Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.1 Regions of Interest. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.4.2 Source of Experimental Dataset . . . . . . . . . . . . . . . . . . . . 9
1.4.3 Specifications of Dataset . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4.4 Data Processing Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.5 Extraction of Spatial Parameters . . . . . . . . . . . . . . . . . . . . 12
1.4.6 Error Metrics and PSNR . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.5 Organization of the Monograph. . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.6 Further Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Spatial Interpolation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Objectives to Review Spatial Interpolation Methods . . . . . . . . . . . 22
2.3 Spatial Interpolation Methods and Their Popularity. . . . . . . . . . . . 22
2.4 Popular Techniques of Spatial Interpolation . . . . . . . . . . . . . . . . . 23
2.4.1 Nearest Neighbors (NN). . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4.2 Inverse Distance Weighting (IDW). . . . . . . . . . . . . . . . . . 25
2.4.3 Ordinary Kriging (OK) . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.4 Universal Kriging (UK) . . . . . . . . . . . . . . . . . . . . . . . . . . 28
2.4.5 Thin Plate Splines (TPS) . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.5 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.1 Spatial Interpolation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
2.5.2 Spatio-Temporal Interpolation. . . . . . . . . . . . . . . . . . . . . . 33
xi
