A Semantic
Earth Observation Data Cube

for Monitoring Environmental Changes during the Syrian Conflict

H. Augustin, M. Sudmanns, D. Tiede & A. Baraldi
University of Salzburg
Department of Geoinformatics - Z_GIS

GI_Forum 2018 | 04.07.2018

What is the Open Data Cube initiative?

Open Data Cube

  • international open-source initiative
  • evolved from Australian Geoscience Data Cube (AGDC)
  • better leverage EO potential
  • support global priority agendas (e.g. UN-SDGs)

Partner Projects

Open Data Cube Development

Operational
  • Australia
  • Columbia
  • Switzerland
In-Development
  • United States
  • Taiwan
  • Uganda
  • Vietnam
  • United Kingdom
  • Georgia
  • Moldova
Figure 1: as of February 2018, 3 operational, 7 in-development, 29 under review
Source: ODC from July 2017 presentation

Automated Workflow

Download

Copernicus Open Access Hub
Sentinel-2

Re-format

6 band stack
resample to 10m
8-bit

Semantic Enrichment

Preliminary Classification with SIAMâ„¢

a priori knowledge-based

decision-tree

fully automatic

based on per-pixel

spectral values

semi-concepts

Example: 48 Semi-concepts

Generate ODC Metadata


						creation_dt: '2018-01-09T13:20:56Z'
						extent:
						  center_dt: '2017-05-28T08:10:43.651Z'
						  coord:
						    ll: {lat: 36.051578712481756, lon: 37.88931628784717}
						    lr: {lat: 36.056674985123045, lon: 39.10836845745278}
						    ul: {lat: 37.04124978409559, lon: 37.87506473735679}
						    ur: {lat: 37.04653228058925, lon: 39.10975932077835}
						  from_dt: '2017-05-28T08:10:43.651Z'
						  to_dt: '2017-05-28T08:10:43.651Z'
						format: {name: ENVI}
						grid_spatial:
						  projection:
						    geo_ref_points:
						      ll: {x: 399960, y: 3990240}
						      lr: {x: 509760, y: 3990240}
						      ul: {x: 399960, y: 4100040}
						      ur: {x: 509760, y: 4100040}
						    spatial_reference: PROJCS["WGS 84 / UTM zone 37N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS
						      84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",39],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32637"]]
						id: 3419725c-7f5c-42f8-82ce-df6f07c1b1fd
						image:
						  bands:
						    18SpCt: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_18SpCt_r88v7.dat}
						    33SharedSpCt: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_33SharedSpCt_r88v7.dat}
						    48SpCt: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_48SpCt_r88v7.dat}
						    96SpCt: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_96SpCt_r88v7.dat}
						    HazePentarnaryMask: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_CAV_HazePentarnaryMask.dat}
						    VegBinaryMask: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_CAV_VegBinaryMask.dat}
						    cBrightness: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_SF_cBrightness.dat}
						    fRatioGreennessIndex: {layer: 1, path: siamoutput/T37SDA_20170528T080611_calrefbyt_lndstlk_SIAMCr_CNV_fRatioGreennessIndex.dat}
						    nodata: {layer: 1, path: T37SDA_20170528T080611_nodata.dat}
						instrument: {name: SIAM}
						lineage:
						  source_datasets:
						    level1:
						      acquisition:
						        groundstation: {code: SGS_}
						      creation_dt: '2017-05-28T08:10:43.000000Z'
						      extent:
						        center_dt: '2017-05-28T08:10:43.651Z'
						        coord:
						          ll: {lat: 36.051578712481756, lon: 37.88931628784717}
						          lr: {lat: 36.056674985123045, lon: 39.10836845745278}
						          ul: {lat: 37.04124978409559, lon: 37.87506473735679}
						          ur: {lat: 37.04653228058925, lon: 39.10975932077835}
						        from_dt: '2017-05-28T08:10:43.651Z'
						        to_dt: '2017-05-28T08:10:43.651Z'
						      format: {name: JPEG2000}
						      grid_spatial:
						        projection:
						          geo_ref_points:
						            ll: {x: 399960, y: 3990240}
						            lr: {x: 509760, y: 3990240}
						            ul: {x: 399960, y: 4100040}
						            ur: {x: 509760, y: 4100040}
						          spatial_reference: PROJCS["WGS 84 / UTM zone 37N",GEOGCS["WGS 84",DATUM["WGS_1984",SPHEROID["WGS
						            84",6378137,298.257223563,AUTHORITY["EPSG","7030"]],AUTHORITY["EPSG","6326"]],PRIMEM["Greenwich",0,AUTHORITY["EPSG","8901"]],UNIT["degree",0.0174532925199433,AUTHORITY["EPSG","9122"]],AUTHORITY["EPSG","4326"]],PROJECTION["Transverse_Mercator"],PARAMETER["latitude_of_origin",0],PARAMETER["central_meridian",39],PARAMETER["scale_factor",0.9996],PARAMETER["false_easting",500000],PARAMETER["false_northing",0],UNIT["metre",1,AUTHORITY["EPSG","9001"]],AXIS["Easting",EAST],AXIS["Northing",NORTH],AUTHORITY["EPSG","32637"]]
						          valid_data:
						            coordinates:
						            - - [509760.0, 4100040.0]
						              - [399960.0, 4100040.0]
						              - [399960.0, 3990240.0]
						              - [509760.0, 3990240.0]
						              - [509760.0, 4100040.0]
						            type: Polygon
						      id: 88d2429c-1204-4b08-9d42-7f889b0749c0
						      image:
						        bands:
						          B01: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B01.jp2}
						          B02: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B02.jp2}
						          B03: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B03.jp2}
						          B04: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B04.jp2}
						          B05: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B05.jp2}
						          B06: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B06.jp2}
						          B07: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B07.jp2}
						          B08: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B08.jp2}
						          B09: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B09.jp2}
						          B10: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B10.jp2}
						          B11: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B11.jp2}
						          B12: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B12.jp2}
						          B8A: {layer: 1, path: GRANULE/L1C_T37SDA_A010085_20170528T081043/IMG_DATA/T37SDA_20170528T080611_B8A.jp2}
						      instrument: {name: MSI}
						      lineage:
						        source_datasets: {}
						      platform: {code: Sentinel-2A}
						      processing_level: Level-1C
						      product_format: {name: SAFE_COMPACT}
						      product_type: S2MSI1C
						platform: {code: SIAM_IL}
						product_type: INFORMATIONLAYERS
	          

Index

linking external data (GDAL formats): indexing

Ingest

create NetCDF tiling scheme and index: ingestion
10km x 10km x 1 time

Analyse

Python API and Jupyter Notebooks

Exploratory Results

  • 3 Sentinel-2 granules
  • ~480 images
  • June 28, 2015 - January 28, 2018
Figure 2: Study area with overlapping relative orbits
Figure 3: Vegetation ccurrance calculation
Figure 4: Vegetation semi-concepts June 28 - September 28, 2015
Figure 5: Vegetation semi-concepts June 28 - September 28, 2016
Figure 6: Vegetation semi-concepts June 28 - September 28, 2017
Figure 7: Vegetation semi-concepts normalised summer difference between 2015 and 2016
Figure 8: Vegetation semi-concepts June 28, 2015 - June 28, 2017
Figure 9: Vegetation semi-concepts June 28, 2015 - June 28, 2016
Figure 10: Vegetation semi-concepts June 28, 2016 - June 28, 2017
Figure 11: Water semi-concepts June 28, 2015 - September 28, 2015
Figure 12: Water semi-concepts June 28, 2016 - June 28, 2017

Thank you!

Hannah Augustin
M.Sc Candidate
University of Salzburg
Department of Geoinformatics (Z_GIS)
hannah.augustin@stud.sbg.ac.at
GitHub: @augustinh22