SPATIAL TEMPORAL FUSION SPECIFIC ENABLER

Stuart E. Middleton, Ajay Chakravarthy, Maxim Bashevoy, Stefano Modafferi, Zoheir SabeurUniversity of Southampton IT Innovation CentreENVIROFI specific enabler17th January 2013

“ENVIROfying” the Future Internet

• WP3 pilot use case• Architecture• Domain specific pre-processing• Aggregation• Temporal fusion• Spatial fusion

OverviewSpatial Temporal Fusion specific enabler

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• WP3 pilot: Ocean Energy & Asset Management• Heterogeneous Data sources

• Observations – Space-borne remote & In-situ sensing• Potential Model data - NPZD ecosystem models to simulate water quality

• Water quality parameter monitoring• Sea water temperature, dissolved oxygen, Nitrogen, turbidity and

sediment concentrations, chlorophyll, microbial exposures…. • Value proposition

• Heterogeneous data aggregation and fusion of respective asynchronous observed water quality parameters’ time series.

• It will assist (a) monitoring water quality at areas with no possible measurements and (b) help trigger risk alerts.

• Spatial-temporal data fusion specific enabler can also be applied to multiple water quality parameters and others.

WP3 pilot use caseSpatial Temporal Fusion specific enabler

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• Key water quality parameters for data fusion• Sea surface temperature and temperature profiles with depth• Salinity concentration levels [in situ/models]• Turbidity [in situ/model sediment concentrations can also be used]• Chlorophyll [measured via satellite from ocean colour]• Nitrate concentration levels• Dissolved Oxygen• Microbial exposures

• 1st demonstrator focus• Spatial Data fusion of sea surface temperature from both remote

(EUMETSAT satellite) and in-situ (ERDDAP smart buoy) sources

WP3 pilot use caseSpatial Temporal Fusion specific enabler

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Remote sensing(e.g. satellite)In-situ sensing

(e.g. smart buoys)

ArchitectureSpatial Temporal Fusion specific enabler

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HTTP RESTful SPSPre-processing

AggregationTemporal fusion

Spatial fusion

OWLIM (metadata)mySQL (data)

Spatial temporal fusion

Domain web portal

WP3 web portal

SPS request- temporal range of interest

- spatial region of interest- phenomenon of interest

Result setsHeterogeneous data sources

EUMETSAT FTP download

ERDDAP portal

Domain specific transcoding

SPARQL/SQL result (numeric)KML/ShapeFile (visualization)

GRIB file(s)

CSV file(s)

CSV file(s)

Web browser

Users (marine)

User

Time, Region

Map

Smart buoy data &Satellite map data(sea surface temperature)

Four levels of data fusionSemantically rich result data

Users request fusion maps viaa domain specific web interface

• Download from domain FTP (EUMETSAT) / web portal (ERDDAP)

• Spatial and temporal filter of datasets for Irish region of interest

• Format conversion - EUMETSAT GRIB2 -> CSV• Special value handling - quality flags (EUMETSAT)• Unit conversion - Celsius (deg)• Output - point data to OWLIM (meta) & MySQL (data)

database tables

Domain specific pre-processingSpatial Temporal Fusion specific enabler

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• Domain concept (ERDDAP, EUMETSAT) mapping to target domain (ERDDAP)

• Aggregate heterogeneous multiple source tables to a coherent aggregated table

• Output – aggregated point data database table

AggregationSpatial Temporal Fusion specific enabler

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• In-situ sensor datasets (ERDDAP)• Point data spatially consistent (buoys)• 2D linear interpolation to create temporally consistent point data

• Remote sensing datasets (EUMETSAT)• Point data spatially inconsistent (map grid points)• Calculate target grid over spatial region of interest• For each timestamp in temporal range of interest calculate target

grid cell mean values (if known)• 2D linear interpolation to create temporally consistent point data

(mean grid cells)• Output - temporally consistent point data database table

Temporal fusionSpatial Temporal Fusion specific enabler

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• Calculate a new target grid over spatial region of interest• For each time slice apply a radial basis function to

interpolate target grid points• Output - spatially and temporally uniform point data

database table• Output - visualizations of data

Spatial fusionSpatial Temporal Fusion specific enabler

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• Spatial fusion• Calculate a new target grid over spatial region of interest• For each time slice apply Radial Basis Functions techniques to

interpolate target grid points while maintaining the integrity of observation data from in situ and remote sensing sources

• Output - spatially and temporally consistent point data database table

• Output - visualizations of data

OverviewSpatial Temporal Fusion specific enabler

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Thank you for your attentionStuart E. Middleton

{sem}@it-innovation.soton.ac.ukwww.ENVIROFI.eu

twitter.com/ENVIROFI

The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement Number 284898

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