An insight of the VHR European data : the processing of the new VHR_IMAGE_2015 using the GHSL tools : a feasibility production test.
Date: 2016-11-21
Institutions: Joint Research Centre
Subjects: data processing, information technology, remote sensing, research method, research report, rural settlement, satellite communications, urban population

An insight of the VHR European data 

The processing of the new 
using 
VHR_IMAGE_2015 
the 
A 
GHSL 
feasibility production test. 

tools. 

Stefano FERRI, Alice SIRAGUSA 

2016 

1 

EUR 28130 EN 

 
 
 
 
 
 
 
 
 
This publication is a Technical report by the Joint Research Centre (JRC), the European Commission’s science and 
knowledge service. It aims to provide evidence-based scientific support to the European policy-making process. 
The scientific output expressed does not imply a policy position of the European Commission. Neither the 
European Commission nor any person acting on behalf of the Commission is responsible for the use which might 
be made of this publication. 

Contact information 
Name: Stefano Ferri 
Address: European Commission, DG Joint Research Centre (JRC) 
Directorate E Space, Security and Migration 
Disaster Risk Management Unit 
Via Enrico Fermi 2749, I-21027 Ispra (VA) Italy, TP 267 
E-mail: stefano.ferri@jrc.ec.europa.eu 
Tel.: +39 0332 786751 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC103093 

EUR 28130 EN 

PDF 

ISBN 978-92-79-62216-8 

ISSN 1831-9424 

doi:10.2788/4022 

Print 

Luxembourg: Publications Office of the European Union, 2016 

© European Union, 2016 

Reproduction is authorised provided the source is acknowledged. 

How to cite:  Stefano FERRI, Alice SIRAGUSA; An  insight of the VHR European  data. The  processing  of the new 

VHR_IMAGE_2015 using the GHSL tools. A feasibility production test; EUR 28130 EN; doi:10.2788/4022 

All images © European Union 2016 

2 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table of Contents 
Abstract .................................................................................................... 4 

Introduction .............................................................................................. 5 

VHR_IMAGE_2015 dataset .......................................................................... 6 

Area of Interest ...................................................................................... 6 

Selected missions .................................................................................... 6 

Spatial Resolution/ Spectral Resolution ...................................................... 7 

Processing levels/ Product Quality ............................................................. 7 

GeoEye-1, WorldView-2, WorldView-3 ..................................................... 7 

Pleiades 1A/1B ..................................................................................... 8 

Deimos-2 and Dubaisat-2 ...................................................................... 9 

Reference year/Acquisitions & Tasking Strategy/ Acquisition windows & 
constraints ........................................................................................... 10 

Delivery ............................................................................................... 11 

DAP and CSCDA .................................................................................... 14 

Results ................................................................................................... 15 

Data download and main characteristics inspection .................................... 15 

NDVI test ............................................................................................. 16 

Use of the EU GHSL tool on the new VHR_IMAGE_2015 ............................. 17 

1st step – Pan sharpening .................................................................... 17 

2nd step aggregation to 2.5m ............................................................... 18 

3rd step from 16 bit to 8 bit .................................................................. 18 

The IQsHR_GHSL_V5b ........................................................................ 19 

Costs comparison (VHR_IMAGE_2015 vs. DWH_MG2b_CORE_03) ............... 21 

Conclusions ............................................................................................. 22 

RAM size requirement ............................................................................ 22 

Retention 2020 ..................................................................................... 22 

Time of processing ................................................................................ 22 

Possible improvements of the algorithm ................................................... 22 

List of figures .......................................................................................... 23 

List of tables............................................................................................ 23 

References .............................................................................................. 23 

3 

 
 
 
 
Abstract  
The European Union and the European Space Agency funding the CSC-Data Access 
project are consolidating predefined needs collected from Copernicus services and 
other  activities  requesting  Earth  Observation  data.  In  2012  the  JRC,  taking 
advantage  of  the  first  wall-to-wall  European  dataset  with  very  high  resolution 
(DWH_MG2b_CORE_03)  and  being  financed  by  DG  REGIO,  derived  the  first 
European  built-up  layer,  with  related  information  using  the  GHSL  tool,  ad  hoc 
tuned on the input dataset (EU GHSL). 

Through the new CSC-Data Access project and its Data Access Portfolio (DAP) and 
using  the  GHSL  tool,  the  JRC  Disaster  Risk  Management  Unit  could  provide 
European wall-to-wall maps of settlements evolution until 2020. 

According to the Data Warehouse document which describes the data availability, 
the acquisition campaign shall be repeated every 3 years taking 2012 as starting 
reference.  Under  this  condition,  at  least  2  European  mosaic  shall  be  produced 
(2015 and 2018 referenced). Considering also the DWH_MG2b_CORE_03, which 
has been already processed and delivered, maps of settlements of three epochs 
(2012, 2015, 2018) would be available for studying urban and rural phenomena 
and trends.  

The report illustrates the results of initial tests conducted using the EU GHSL tool 
on  the  new  VHR_IMAGE_2015,  which  promises  to  be  much  more  accurate  and 
better documented than the previous one. 

4 

 
 
 
 
Introduction 

The European Union and the European Space Agency funded the CSC-Data Access 
project  in  the  frame  of  the  European  Community's  Seventh  Framework 
Programme FP7/2007-2013 and the Multiannual Financial Framework Programme 
(MFF).  It  is  an  integral  part  of  the  GMES/Copernicus  Space  Component 
Programme. 

The  CORE  datasets  aim  at  consolidating  predefined  needs  collected  from 
Copernicus  services  and  other  activities  requesting  Earth  Observation  data 
whether financed by the Union or related to Union’s policies, like Union financed 
research projects and the activities of Union agencies (EEA, EMSA, SatCen, etc.) 

For an initial period of 2007-2011, data access management was funded through 
a  data  access  grant  between  the  EC  and  ESA.  Through  the  CSC-Data  Access 
project and the new Data Access Portfolio (DAP) and using the GHSL tool, the JRC 
Disaster  Risk  Management  Unit  could  provide  European  wall-to-wall  maps  of 
settlements evolution until 2020. 

In 2012 the JRC, taking advantage of the first wall-to-wall European dataset with 
very  high  resolution  (DWH_MG2b_CORE_03)  and  being  financed  by  DG  REGIO, 
derived the first European built-up layer, with related information using the GHSL 
tool, ad hoc tuned on the input dataset (EU GHSL).  

Today the aim is to run the EU GHSL tool to process the new VHR_IMAGE_2015 
dataset and to derive updated settlement characteristics. 

The  VHR_IMAGE_2015  is  a  new  optical  VHR  multispectral  and  panchromatic 
coverage dataset over Europe. It is financed by the European Union as part of the 
GMES/Copernicus Space Component Programme. 

Because  of  its  characteristics  it  can  be  considered  an  update  of  the 
DWH_MG2b_CORE_03, used to produce the European Settlement Map (ESM) [1]. 
Both  the  DWH_MG2b_CORE_03  and  the  VHR_IMAGE_2015  have  very  high 
resolution1 and European extent. 

The  VHR_IMAGE_2015  is  part  of  a  CORE  dataset  that  aims  at  consolidating 
predefined  needs  collected  from  Copernicus  services  and  other  activities 
requesting  Earth  Observation  data  whether  financed  by  the  Union  or  related  to 
policies of the Union, like EU financed research projects and the activities of Union 
agencies (EEA, EMSA, SatCen, etc.) 

In  the  long  term,  according  to  the  Data  Warehouse  document2,  the  acquisition 
campaign shall be repeated every 3 years taking 2012 as starting reference. Under 
this condition, at least 2 additional European mosaics shall be produced (2015 and 
2018).  Considering  also  the  DWH_MG2b_CORE_03,  which  has  been  already 

1 DWH_MG2b_CORE_03 has resolution of 2.5m and the VHR_IMAGE_2015 has resolution of 0.5m 
panchromatic and 2m multispectral. 
2 https://spacedata.copernicus.eu/documents/12833/14545/DAP_Release_Phase_2 

5 

 
 
                                       
processed and delivered, maps of settlements of three epochs (2012, 2015, 2018) 
could be produced for studying urban and rural phenomena and trends.  

The EU GHSL tool [2], optimized in the URBA project3, is the best available tool to 
process the new dataset. 

The objectives of this report are: 

  Describe the technical specification of the new VHR_IMAGE_2015 dataset. 
  Obtain the acquisition program (schedule) 
  Analyse the provider FTP dataset structure (directory organization) 
  Test scenes selection methods (geographical based selection) 
  Download timing tests 
  Pre-processing and processing using the GHSL tools 
  Results report and cost estimation of processing the whole dataset 

VHR_IMAGE_2015 dataset  
The technical details of the VHR_IMAGE_2015 are presented here as reported in 
the dedicated Copernicus web page4 . 

Area of Interest 

39 European states (EEA39, see Annex 1) including all islands of those countries 
plus  French  Overseas  Department  [French  Guiana,  Martinique,  Guadeloupe, 
Mayotte, Reunion] but excluding French Overseas Territories. The overall AOI is 
extended by a buffer of 2 nautical miles. 

The overall AOI of interest is divided into 137 Large Regions. 

Selected missions 

The  satellites  selected  for  the  generation  of  the  mosaic  are:  Pléiades  1A  &  1B, 
WorldView-2, WorldView-3, GeoEye-1, Deimos-2, Dubaisat-2. 

The AOI has been allocated as follows: 

• 

• 

• 

Pléiades 1A & 1B, 89 large regions 

Deimos-2/Dubaisat-2, 17 large regions 

WorldView-2, WorldView-3, GeoEye-1, 31 large regions 

3 URBA PROJECT WPK ID 1193 – European Commission, Joint Research Centre 
4 https://spacedata.copernicus.eu/web/cscda/dataset/-/asset_publisher/uDd0At6AeU7H/content/optical-vhr-
multispectral-and-panchromatic-coverage-over-europe-vhr_image_2015- 

6 

 
 
 
 
 
                                       
Spatial Resolution/ Spectral Resolution 

VHR1 panchromatic/VHR2 multispectral (bundle products) 

Spectral bands: 

• 

GeoEye-1: Pan, Blue, Breen, Red, NIR 

WorldView-2: Pan, Coastal, Red, Blue, Red Edge, Green, NIR-1 and 2, 

• 
Yellow 

• 

• 

• 

WorldView-3: Coastal, Red, Blue, Red Edge, Green, NIR-1 and 2, Yellow 

Pleiades: Pan, Blue, Green, Red, NIR 

Deimos-2 and Dubaisat-2: Pan, Blue, Green, Red, NIR 

Processing levels/ Product Quality 

L1 (system corrected), L3 (ortho) bundle products 

• 

• 

• 

• 

Presence of perennial snow and glaciers is to be expected in products. 

Sun elevation angle higher than 20° 

Off-nadir viewing angle less than 30°. 

Reference: CORE_003 delivered during DWH phase 1 

Cloud coverage: 5% and 20% per production unit/delivered product 

• 
according to zones 

• 

Haze: acceptability assessed by EEA 

GeoEye-1, WorldView-2, WorldView-3 

Radiometric resolution: 16 bits 

Geometric resolution: 50cm 

File Format: GEOTIFF (fully compliant with TIFF version 6.0) 

Dynamic Range Adjustment: OFF 

• 

• 

• 

• 

Ortho-rectified at-sensor radiance values (resampled using cubic 

• 
convolution method) 

• 

• 

• 

Projection/Datum: UTM/WGS84 (for Ortho Ready Standard product); 
LAEA [EPSG code 3035] or National projection (for Ortho product) 

Datum: WGS84 (for Ortho Ready Standard Product); ETRS89 [EPSG code 
3035] (for Ortho product); National projection. 

Tiling: 16k 

7 

 
 
 
 
• 

GCP file with respect to reference data set 

Ortho-correction: Ortho ready processing with basic corrections such as 
rectilinear geometry, radiometric distortion (i.e. source data). 

• 

• 

• 

Ortho-rectification full-parametric or making use of RPC functions 

Grid alignment: GeoTIFF key: The GeoTIFF key "GTRasterTypeGeoKey" 
will have the value 1, corresponding to "RasterPixelIsArea". 

Pixel alignment: the values of the coordinates for the upper-left corner (x 
and y) of the upper-left pixel of an image dataset will be an integer 
multiple of the pixel size (resolution) in the corresponding direction (x and 
y). 

Ortho-rectification accuracy: Relative accuracy less than 1-pixel difference from 
DWH phase 1 CORE_003. 

Metadata: INSPIRE compliant metadata for all processing levels in XML format 
will be delivered once INSPIRE metadata developments are concluded, expected 
within 2015. 

Pleiades 1A/1B 

Level 1A: System corrected and at-sensor radiometric and geometric calibrated 
data 

Ortho: 

• 

• 

• 

• 

Reference 3D DEM (on areas on which Reference 3D is not available, 
SRTM DEM will be used) 

Resampling/Native resolution =0.50m 

Resampling using cubic convolution method 

GCP file with reference dataset: available in CDS 

Radiometric correction: 

• 

Calibration parameters enabling the conversion of DN into spectral 
radiance+ solar spectral radiance 

Orth-correction: 

• 

• 

• 

Ortho ready processing includes basic corrections such as rectilinear 
geometry, radiometric distortion 

European projection: ETRS89/LAEA 

Grid alignment: GeoTIFF key 

8 

 
 
  
 
• 

• 

Pixel alignment: UP corner of the UL pixel of an image dataset shall be an 
integer multiple of the pixel size 

Geo-location relative accuracy less than 1 pixel wrt. CORE_03 (same 
reference which is Reference 3D) 

Metadata: DIMAP V2 and DIMAP V2 tailored, which is partially compliant with 
INSPIRE until INSPIRE metadata developments are concluded, expected within 
2015. 

File Format: geoTIFF v6 or JPG2000 optimised 

Deimos-2 and Dubaisat-2 

Level1 (system correction) (L1B): 

System corrected and at-sensor radiometric and geometric calibrated 

• 
data; 

• 

Ortho layer, (i.e. x, y coordinates (ETRS89)) 

Ortho-rectified Level to ETRS89/LAEA and National projection (L1C): 

• 

Reference: CORE_003 delivered under DWH V1 

Ortho-rectified at-sensor radiance values (resampled using cubic 

• 
convolution method); 

• 

GCP file with respect to reference data set 

Radiometric resolution: 

Both Deimos-2 and Dubaisat-2 have a bit depth of 10-bits signal dynamic range. 

Radiometric correction: 

Radiometric calibrated data with calibration parameters enabling the conversion 
of image digital numbers (DN) into spectral radiance (W/m2/sr/um). Solar 
spectral irradiances within each spectral band and the solar elevation for each 
scene are provided to convert the image DNs into TOA (Top Of Atmosphere) 
radiances values 

Orthocorrection 

Ortho-rectification making use of RPC. Default DEM is SRTM V3. 

Ortho-rectification accuracy: less than 1-pixel difference from DWH phase 1 
CORE_003 

File Format and Structure: geoTIFF fully compliant with TIFF version 6 

Metadata: DIMAP v1 metadata for both L1B and L1C processing levels until 
INSPIRE metadata developments are concluded, expected within 2015. 

9 

 
  
 
 
Reference year/Acquisitions & Tasking Strategy/ Acquisition 
windows & constraints 

Reference year: 2015 +/-1 year. 

Spot-5 data are available only in 2014. 

Systematic tasking took place in 2014 for Resourcesat-2 over Europe within the 
extended windows, and will continue in 2015 within the extended windows. 

Acquisition  windows  are  defined  in  Annex  25.  The  starting  and  ending  dates  of 
these windows can be subject to modifications from CSP to ESA only in case of 
late  onset  of  vegetation,  so  that  ESA  can  inform  CCMs  with  a  ten-day  advance 
notice for any change of either an earlier start and/or later end. It is assumed that 
such changes shall be the exception and take place on country basis. 

5 https://spacedata.copernicus.eu/documents/12833/14545/DAP_Release_Phase_2 

10 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                       
Delivery 

Data deliveries are being made at the end of each acquisition window, per large 
regions  and  no  later  than  90  days  after  acceptance,  assuming  progressive 
production approval by EEA. Further information can be found in the DWH Phase 
CORE datasets status report.  

Delivery Mechanism 

FTP@CDS: data available at the Core Infrastructure (CDS) 

Figure 1 - Large regions map and providers 

In the ftp, folders are organized by counties at the 1st directory level (Figure 1) 

Figure 2 - ftp 1st level directory structure 

11 

 
 
 
 
The 2nd directory level the folders are organized according to the large region 
encoding (Figure 3, Figure 4)  

Figure 3 - Large regions map with codes 

Figure 4 – ftp 2nd level directory structure 

12 

 
 
 
 
 
The 3rd directory level is subdivied according to the projection (Figure 5) 

Figure 5 - ftp 3rd level directory structure 

The 4th directory level there are the scenes compressed (Figure 6). 

Figure 6 - ftp 4th level directory structure 

At the moment a geo-index of the scenes in the 4th directory level is not available. 
This makes difficult to choose an image of a specific location or of a city. However, 
an alternative access to the dataset exists: the Copernicus Client (EOLiSA/GCL), 
a standalone tool (Figure 7). With the Copernicus Client, queries can be made for 
a variety of parameter, such as dataset, mission, resolution etc., while identifying 
your Area of Interest and time period (see Data Download guidelines6). 

6 
https://spacedata.copernicus.eu/documents/12833/20397/Data+Discovery+and+Downl
oad+Guidelines 

13 

 
 
 
 
                                       
Figure 7 - EOLiSA/GCL interface 

DAP and CSCDA 

The images are made available to the Copernicus Users in response to their Earth 
Observation  data  requirements.  The  Data  Access  Portfolio  Document  (DAP) 
defines which dataset are available; it is the baseline for the implementation of 
the Copernicus Space Component Data Access (CSCDA) Services. 

The categories of Copernicus Users eligible for accessing data from the Copernicus 
Space Component are defined and maintained by the European Commission (EC) 
in the Data Warehouse requirements document. 

The  Data  Warehouse  Requirements  (Version  2.0)  presents  the  Copernicus 
requirements for space-based Earth Observation data for the period covered by 
the Copernicus Regulation. It contains the requirements collected from Copernicus 
services and other users requesting Earth Observation data, whether financed by 
the Union or related to policies of the Union, like Union-financed research projects 
and the activities of Union agencies (EEA, EMSA, SatCen (previously EUSC), etc.) 

The  access  rights  of  each  user  category  are  part  of  the  agreements  with  all 
Contributing Missions Entities negotiated by ESA; they are reported in (Table 1) 
for  both  CORE  and  Additional  datasets.  Download  and  View  rights  are  defined 
where they apply for a dataset or dataset type (CORE, ADD), per user category. 

14 

 
 
 
Results 

Table 1- Access rights 

Data download and main characteristics inspection 

The download time of a scene can vary according to scene’s size. The average size 
of a scene with European projection (L3_European) is 2.3 GB. The download takes 
on average 15 minutes. The scenes are archived with tar7, and once downloaded 
they need to be extracted. 

The scene downloaded for this test refers to the city of Turin (Italy). It is a Pleiades 
1A  organized  in  two  files:  one  multispectral  (Figure  8)  2m  pixel  resolution  and 
bands  RED,  GREEN,  BLU,  NIR  and  a  pan-chromatic  (Figure  9)  0.5m  pixel 
resolution. Both files are compressed JPEG2000 and complete with metadata. 

The  latest  update  (15th  August  2016)  reports  that  most  of  the  dataset  was 
delivered to the CDS archive (Error! Reference source not found.). 

7 https://www.gnu.org/software/tar/tar.html 

15 

 
 
                                       
Table 2 - Last acquisition report 15th Aug. 2016 

The number of tar files in the ftp is 12140 and their size is 44.32 TB, since the tar 
is just an archive the total size of the dataset after the extraction will not increase. 

Figure 8 - Turin false colour (resolution 2m) 

Figure 9 - Turin panchromatic (resolution 
0.5m) 

NDVI test 

A  new  characteristic  introduced  by  this  new  dataset,  not  present  in  the 
DWH_MG2b_CORE_03,  it  is  the  calibration  and  radiometric  correction.  This 
feature,  not  essential  for  the  GHSL  algorithm,  could  however  increases  the 
accuracy  of  the  overall  process  and  make  simpler  the  extraction  of  the  green 
components.  

The NDVI  extracted  from  the  multispectral  layer  highlight  clearly  the  vegetated 
area. The values and ranges match with literature [3]. Appling thresholds, values 

16 

 
 
 
 
 
 
greater  than  0.3  well-represent  the  vegetation  components  (Figure  10),  while 
negative values well describe the water presence (Figure 11). 

Figure 10 - NDVI resolution 2m 

Figure 11 - Water resolution 2m 

Raw L3_European Mosaic space estimation 

Use of the EU GHSL tool on the new VHR_IMAGE_2015 

The workflow of the  GHSL tool used to produce the first VHR  mosaic of Europe 
was optimised for the processing of the DWH_MG2b_CORE_03 datasets, made of 
SPOT-5/6 pan sharpened images (NIR, RED, GREEN) with 2.5m pixel resolution 
and  8bit.  Therefore, 
the  new 
VHR_IMAGE_2015. 

the  workflow  had 

to  be  adapted 

to 

The  adaptation  consists  in  making  the  pan  sharpening,  aggregating  the 
VHR_IMAGE_2015 at 2.5m, and organizing the bands in the right sequence. 

1st step – Pan sharpening 
The  fusion  of  the  multispectral  bands  with  the  panchromatic  is  done  using  the 
ArcGIS tool “Create Pan-sharpened Raster Dataset” using SIMPLE_MEAN as Pan-
sharpening Type. The Simple Mean transformation method applies a simple mean 
averaging equation to each of the output band combinations: respect other pan-
sharpening  transformation  its  equation  can  be  easily  integrated  into  a  Matlab 
workflow. 

17 

 
 
 
 
 
Figure 12 - ArcGis settings Pan-sharpening 

2nd step aggregation to 2.5m 
The aggregation is done using the GDAL utility tool “gdalwarp” using average as 
resampling operator. 

gdalwarp -tr 2.5 2.5 -r average -co "COMPRESS=LZW" --config GDAL_CACHEMAX 2000 
PH1A_PHR_BUN__3_20150811T103101_20150811T103104_TOU_1234_643c_psh.tif 
PH1A_PHR_BUN__3_20150811T103101_20150811T103104_TOU_1234_643c_psh_2m5_average.tif 

3rd step from 16 bit to 8 bit 
The rescaling from 16bit to 8bit is done because the GHSL Tool contains a function 
named PANTEX which accepts as input only 8bit datasets 
%matlab code 
addpath(genpath('/mnt/public/Isferea/Management/Public Tools/')); 
im = 
imread('PH1A_PHR_BUN__3_20150811T103101_20150811T103104_TOU_1234_643c_psh_2m5_average.tif'); 
geinfo = 
geoiminfo('PH1A_PHR_BUN__3_20150811T103101_20150811T103104_TOU_1234_643c_psh_2m5_average.tif
'); 

for i = 1:4 
    bandi = im(:,:,i); 
    maxVal = mean(bandi(:)); 
    stdval = std(single(bandi(:)),'omitnan'); 
    im(:,:,i) = (single(im(:,:,i)) ./ (maxVal+2*stdval)) .* 255;    
end 

NIR = im(:,:,4); 
RED = im(:,:,1); 
GREEN = im(:,:,2); 

imres = NIR; 
imres = cat(3,imres,RED); 

18 

 
 
 
 
 
 
 
imres = cat(3,imres,GREEN); 

geoimwrite(uint8(imres),'PH1A_PHR_BUN__3_20150811T103101_20150811T103104_TOU_1234_643c_psh8bi
t_matlab.tif',geinfo); 

After this step, the image is ready to be processed using the GHSL tool. 

The IQsHR_GHSL_V5b 
The  GHSL  tool  optimized  for  the  European  dataset  is  a  Matlab  tool  named 
IQsHR_GHSL_V5b.  It  takes  as  input  a  parameter  file  with  all  the  information 
needed for the processing. The parameter file used to process the sample image 
over Torino is shown below. 

input#NIR#string#/mnt/processing/02_REGIONAL/URBA_GHSL_EU/ver_7/VHR_IMAGE_2015/IT/60/L3_European/PH1A_PHR_BUN__3_201
50811T103101_20150811T103104_TOU_1234_643c_psh8bit_matlab.tif#1 
input#GREEN#string#/mnt/processing/02_REGIONAL/URBA_GHSL_EU/ver_7/VHR_IMAGE_2015/IT/60/L3_European/PH1A_PHR_BUN__3_2
0150811T103101_20150811T103104_TOU_1234_643c_psh8bit_matlab.tif#3 
input#RED#string#/mnt/processing/02_REGIONAL/URBA_GHSL_EU/ver_7/VHR_IMAGE_2015/IT/60/L3_European/PH1A_PHR_BUN__3_201
50811T103101_20150811T103104_TOU_1234_643c_psh8bit_matlab.tif#2 
output#features#string#/mnt/processing/02_REGIONAL/URBA_GHSL_EU/ver_7/VHR_IMAGE_2015/IT/60/L3_European/out.tif# 
output#xml#string#/mnt/processing/02_REGIONAL/URBA_GHSL_EU/ver_7/VHR_IMAGE_2015/IT/60/L3_European/out.xml# 
output#mat#string#/mnt/processing/02_REGIONAL/URBA_GHSL_EU/ver_7/VHR_IMAGE_2015/IT/60/L3_European/out.mat# 
param#cloudmask_folder#string#\NETAPP1\iq\process\projects\DG_2012_REGIO_GMES\cloud\VER1-0# 
param#showmes#scalar#1# 
param#res_out#scalar#-1# 
param#res_out_type#string#box# 
param#res_proc#scalar#2.5# 
param#res_proc_type#string#bicubic# 
param#orig_res#scalar#1# 
param#Feat_mrph_lambda#vector#0,50,150,250,350,450,550,650,750,850,950,1050,1150,1250,1350,1450,1550,1650,1750,1850,1950,20
50,2150,2250,2350,2450,2550,2650,2750,2850,2950,3050,3150,3250,3350,3450,3550,3650,3750,3850,3950,4050,4150,4250,4350,4450,4
550,4650,4750,4850,4950# 
param#Feat_ptx_wsize#vector#9,9# 
param#Feat_tilesize#vector#3000,3000# 
param#Feat_VegMfactor#scalar#0# 
param#Feat_VegRescaleMin#scalar#-0.3# 
param#Feat_VegRescaleMax#scalar#0.5# 
param#Feat_ptxRescaleMax#scalar#1000# 
param#Training_RescaleMin#scalar#0# 
param#Training_rescaleMax#scalar#30# 
param#Training_name#string#_sealing_v2# 
param#Training_res#scalar#50# 
param#Training_CloseOpenSE#scalar#4# 
param#Training_areaHF#scalar#10# 
param#Training_dilateradius#scalar#1# 
%%param#Training_smoothradius#scalar#15# 
param#Landcover_name#string#_CLC_2000_ORG# 
param#Landcover_bu_off_list#vector#7,8,30,31,34,38,40,41,42,43,44# 
param#Landcover_bu_list#vector#1,2,3# 
param#Landcover_buscatter_list#scalar#2# 
param#Landcover_water_list#vector#40,41,42,43,44# 
param#Landcover_in_veg_list#vector#10,16,18,22,23,24,25,26,27,28,29# 
param#Landcover_off_veg_list#vector#1,3,4,5,7,8,9,31,33,34# 
param#LearnPantex_Tilesize#vector#400,400# 
param#LearnPantex_PriorSmooth#vector#5,5# 
param#LearnPantex_Datamax#scalar#255# 
param#LearnPantex_Datamin#scalar#1# 
param#LearnPantex_Nbins#scalar#256# 
param#LearnPantex_Mintrsh#scalar#3# 
param#LearnPantex_f_bias#vector#0,0# 
param#LearnPantex_f_gain#scalar#0.9# 
param#LearnPantex_smooth#vector#3,3# 
param#LearnPantex_BoudZeroPrior#scalar#1# 

19 

 
 
 
 
 
param#LearnPantex_InjectApriori#scalar#1# 
param#LearnPantex_InjectAprioriWeight#scalar#100# 
param#LearnPantex_InjectAprioriKernel#scalar#15# 
param#LearnMorpho_stdf1#scalar#-0.5# 
param#LearnMorpho_stdf2#scalar#1# 
param#LearnMorpho_SSLBUdomain_trsh#scalar#99# 
param#LearnVegNaturalAreas#scalar#0# 
param#LearnVegStdTrsh#scalar#210# 
param#LearnVeg_stdf1#scalar#0# 
param#LearnVeg_stdf2#scalar#2# 
param#ptxClosingSize#scalar#40# 
param#ptxFeatheringSize#scalar#21# 
param#MorphoBuiltEnphasize#scalar#0# 
param#BUMaxInnerHolePix#scalar#24000# 
param#NoCPUs#scalar#8# 
param#useParallelFeatureExtraction#scalar#0# 
param#useParallelLearnClassification#scalar#0# 
param#srid#string#3035# 
param#save_intermediate_results#scalar#1# 

The execution command is: 
/mnt/public/Isferea/Management/Public\ Tools/MATLAB_mirrored/IQ_scripts/2nd\ 
generation/Linux/IQsHR_GHSL_V5b/run_IQsHR_GHSL_V5b.sh /usr/local/MATLAB/MATLAB_Compiler_Runtime/v80 -F 
/mnt/processing/02_REGIONAL/URBA_GHSL_EU/ver_7/VHR_IMAGE_2015/IT/60/L3_European/param.txt 

The execution time of the process took 186.9138 seconds. It is much less than 
the  time  needed  to  process  a  scene  in  the  old  DWH_MG2b_CORE_03  datasets. 
The  increase  of  speed  can  be  attributed  to  the  smaller  size  (in  pixels)  of  the 
scenes. 
The result of the test on the area of Turin (Torino, 8 – Aug – 2015) (Figure 13) 
looks  comparable  to  the  one  done  using  the  SPOT  (Torino,  20  –  Oct  –  2011) 
(Figure 14). No major changes in built-up are detected in the time lapse.  

Torino,  20 – Oct – 2011 

Torino,  8 – Aug – 2015 

Figure 13 - GHSL output OLD dataset (2.5m) 

Figure 14 - GHSL output NEW dataset (2.5m) 

20 

 
 
 
 
 
 
 
 
Costs comparison (VHR_IMAGE_2015 vs. DWH_MG2b_CORE_03) 

In Table 3 the main characteristics and estimated costs of processing the new 
dataset VHR_IMAGE_2015 against the DWH_MG2b_CORE_03 datasets are 
compared. 

Number of 
scenes 
Average size of a 
scene (pixel) 
Average size of a 
scene (meter) 

Resolution 

FTP ESA 
retention 
Download into 
JRC CID Portal 
Disk Space 
needed for raw 
data (source) 
Disk Space 
needed for the 
output at 2.5m 
PSH 
Uncompressed 
Size (minimum 
memory  needs 
for processing) 

VHR_IMAGE_2015  
12140 

DWH_MG2b_CORE_03 
3800 

PAN: 44207, 41563 
MUL: 11043, 10391 
PSH: 8841, 8313 

PSH: 31191, 30513 

22km, 22km 

78km, 76km 

PAN: 0.5m 
MUL: 2m 
PSH: 2.5 

until 2020 

Not planned 

44TB 

58 GB 

PSH: 2.5m 

unknown 

Complete download of all 
the dataset 

8 TB 

58 GB 

560 MB 

2.66 GB 

Table 3 - Cost comparison OLD/NEW 

21 

 
 
 
 
 
 
Conclusions 
The characteristics of this dataset offer new research opportunities. Some of them 
are  technical  challenges  related  to  massive  processing  of  big  data,  other  are 
thematic  research  in  urbanization  at  very  high  resolution,  such  as  changes  in 
shape and size of settlements across time. Contrary what one might expected the 
cost of processing does not seem to increase, due to an externalization of the cost 
to store the raw data. 

RAM size requirement 

The average size in pixel of an image of the new dataset (PAN) is comparable to 
the one of the old dataset, but the pan-sharpened version is much smaller. This 
is a very important characteristic to be taken into consideration when processing 
the  files.  Because  the  processing  works  at  2.5  meter,  also  machines  with  low 
amount of memory can process the raw input data, or more powerful machines 
can  process  more  images  simultaneously.  The  low  memory  need  positively 
influences cluster management.  The size of a raw PSH in RAM memory is about 
560MB  which  can  easily  be  contained  in  memory  in  any  commercial  computers 
nowadays. 

Retention 2020 

The new dataset contains much more images than the old one, and its raw weight 
(raw data) on the disk is 8 times bigger than the DWH_MG2b_CORE_03. Because 
it will be retained in the ESA ftp until the 2020, there is no need to download the 
whole dataset, but just the image to be processed. 

Time of processing  

Although the estimated time needed to process the new dataset will be similar to 
the old one, the number of error due to lack of memory (in concurrent operation) 
will considerably be reduced thanks to their size.  

Possible improvements of the algorithm  

The characteristics of calibration and radiometric correction  of the new dataset, 
open to further improvements of the GHSL tool, which now can rely on a consistent 
spectral  signature  across  the  scenes.  For  example,  the  classification  of  the 
vegetation  using  the  NDVI  will  be  more  precise  and  accurate.  The  algorithm  to 
extract the urban green will be faster because the spectral signature can be used 
in place of a complex, slow learning/rescaling process that needs reference layers. 
The built-up detection algorithm can take advantage of known spectral building 
characteristics in its morphological operation of classification. 

22 

 
 
 
 
 
 
 
 
 
List of figures 
Figure 1 - Large regions map and providers ................................................ 11 
Figure 2 - ftp 1st level directory structure .................................................... 11 
Figure 3 - Large regions map with codes ..................................................... 12 
Figure 4 – ftp 2nd level directory structure ................................................... 12 
Figure 5 - ftp 3rd level directory structure .................................................... 13 
Figure 6 - ftp 4th level directory structure .................................................... 13 
Figure 7 - EOLiSA/GCL interface ................................................................. 14 
Figure 8 - Torino false colour (resolution 2m) .............................................. 16 
Figure 9 - Torino panchromatic (resolution 0.5m) ........................................ 16 
Figure 10 - NDVI resolution 2m ................................................................. 17 
Figure 11 - Water resolution 2m ................................................................ 17 
Figure 12 - ArcGis settings Pan-sharpening ................................................. 18 
Figure 13 - GHSL output OLD dataset (2.5m) .............................................. 20 
Figure 14 - GHSL output NEW dataset (2.5m) ............................................. 20 

List of tables 
Table 1- Access rights............................................................................... 15 
Table 2 - Last acquisition report 15th Aug. 2016 ........................................... 16 
Table 3 - Cost comparison OLD/NEW .......................................................... 21 

References 
[1]  B. Armin, D. M. Giovanni, and A. Paer, “Specifications of view services for 

GMES Core_003 VHR2 coverage,” 2012. 

[2]  A. J. Florczyk, S. Ferri, V. Syrris, T. Kemper, M. Halkia, P. Soille, and M. 

Pesaresi, “A New European Settlement Map From Optical Remotely Sensed 
Data,” IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 9, no. 5, pp. 
1–15, 2015. 
J. W. and D. Herring, “Measuring Vegetation (NDVI &amp; EVI) : Feature 
Articles,” 2000. 

[3] 

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doi:10.2788/4022 

ISBN 978-92-79-62216-8