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“ENVIROfying” the Future Internet. Spatial Temporal Fusion SPECIFIC ENABLER. Stuart E. Middleton, Ajay Chakravarthy , Maxim Bashevoy , Stefano Modafferi , Zoheir Sabeur University of Southampton IT Innovation Centre ENVIROFI specific enabler 17 th January 2013.

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spatial temporal fusion specific enabler

“ENVIROfying” the Future Internet

Spatial Temporal Fusion SPECIFIC ENABLER

Stuart E. Middleton, Ajay Chakravarthy, Maxim Bashevoy, Stefano Modafferi, ZoheirSabeur

University of Southampton IT Innovation Centre

ENVIROFI specific enabler

17th January 2013

overview

Spatial Temporal Fusion specific enabler

Overview

  • WP3 pilot use case
  • Architecture
  • Domain specific pre-processing
  • Aggregation
  • Temporal fusion
  • Spatial fusion
wp3 pilot use case

Spatial Temporal Fusion specific enabler

WP3 pilot use case

  • 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 case1

Spatial Temporal Fusion specific enabler

WP3 pilot use case

  • 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

Remote sensing

(e.g. satellite)

In-situ sensing

(e.g. smart buoys)

architecture

Spatial Temporal Fusion specific enabler

Architecture

Four levels of data fusion

Semantically rich result data

Users request fusion maps via

a domain specific web interface

Smart buoy data &

Satellite map data

(sea surface temperature)

domain specific pre processing

Spatial Temporal Fusion specific enabler

Domain specific pre-processing

  • 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
aggregation

Spatial Temporal Fusion specific enabler

Aggregation

  • 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
temporal fusion

Spatial Temporal Fusion specific enabler

Temporal fusion

  • 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
spatial fusion

Spatial Temporal Fusion specific enabler

Spatial fusion

  • 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

Visualization of fused sea surface temperature data

in the West coast of Ireland (single timeslice)

overview1

Video of fused sea surface temperature data

in the West coast of Ireland (many timeslices)

Spatial Temporal Fusion specific enabler

Overview

  • 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
thank you for your attention
Thank you for your attention

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

Stuart E. Middleton

{sem}@it-innovation.soton.ac.uk

www.ENVIROFI.eu

twitter.com/ENVIROFI