A Comparative Analysis of “Urban Expansion” using Remotely

Sensed Data of CORINE Land Cover and Global Human Settlement

Layer in Estonia

Najmeh Mozaffaree Pour

and Tõnu Oja

Department of Geography, University of Tartu, Tartu, Vanemuise 46, Tartu, Estonia

Department of Geography, University of Tartu, Tartu, Estonia

Keywords: Urban Expansion, Remote Sensing, CORINE (Coordination of Information on the Environment) Land Cover,

Global Human Settlement (GHSL)- Built-up Layer, Google Earth Engine (GEE), Estonia.

Abstract: Monitoring urban expansion is important because the policy makers in cities must detect the changes to

provide services and manage resources for urban dwellers. In this study we analyse the built-up areas extracted

from very high-resolution images of two important databases of CORINE land cover and GHSL; Built-Up

Grid to map urban expansion at local level of cities of Tallinn and Tartu in their context (County) in Estonia.

The reason for selecting these datasets was the representation of an available temporal data in many timespans

which allowed extracting urban expansion in our case studies. The analysis was carried out over a subset of

these datasets in ArcGIS environment and the data of GHSL- Built-Up Grid was extracted from Google Earth

Engine platform. Therefore, the results showed that there was an increase in the amounts of built-up areas and

its rate in these two counties while based on these two databases the results were not similar in areas and cells

but similar in rate and growth patterns.

1 INTRODUCTION

Monitoring urban expansion is important because the

policy makers in cities must detect the changes to

provide services and manage resources for urban

dwellers. While urbanization is universal, changes in

land cover and landscape pattern around the globe are

irrevocable. Therefore, the spatial and temporal

characteristics and consequences of urbanization

must be scientifically understood. A widely used

technique for detecting the urban expansion is

remotely sensed data. Remote sensing (RS)

technology enhances the availability of spatially

explicit and temporally consistent land use and land

cover change information (Herold et al., 2002;

Michishita et al., 2012).

Satellite remote sensing offers a tremendous

advantage over historical maps or air photos, as it

provides recurrent and consistent observations over a

large geographical area, reveals explicit patterns of

land cover and land use, and presents a synoptic view

of the landscape (Jensen et al., 1999). Increased



https://orcid.org/ 0000-0001-9969-6631

https://orcid.org/0000-0001-7603-220X

availability of very high-resolution remotely sensed

images in recent years, coupled with advancements in

high-performance computing resources and efficient

image processing algorithms have fostered the

development of high-resolution human settlement

datasets (Cheriyadat et al., 2007, Vijayaraj et al.,

2007, Patlolla et al., 2012).

The availability of regional and global land cover

products, provides us with a wide variety of options

to utilize for our own respective research. However,

these products differ on the basis of the methodology

used to create them and the classification systems

used to generate the several land use partitions

(Defries et al., 1994; Fritz et al., 2010).

Starting with the GHS-BUILT product, it was the

result of a large scale experiment conducted by the

European Commission in 2014 aimed at extracting

information on built-up areas from Landsat (Pesaresi

et al., 2016), producing the first multi-temporal

explicit description of the evolution of built-up

presence in the past 40 years. The Landsat product

contains a set of multi-temporal and multi-resolution

Mozaffaree Pour, N. and Oja, T.

A Comparative Analysis of “Urban Expansion” using Remotely Sensed Data of CORINE Land Cover and Global Human Settlement Layer in Estonia.

DOI: 10.5220/0009195101430150

In Proceedings of the 6th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2020), pages 143-150

ISBN: 978-989-758-425-1

143

grids. The main product is the multi-temporal

classification layer on built-up presence derived from

the Global Land Survey (GLS) Landsat image

collections (GHSL homepage).

In particular, the GHSL project is focused on

innovative automatic image information extraction

processes, using metric and decametric scale satellite

data input (Pesaresi et al., 2013) and making the

information gathering is independent from any

rural/urban prior abstract definition (Pesaresi et

al.,2015). As Florczyk et al., 2016 mentioned “The

main characteristics of the developed methodology

for GHSL are a scene-based processing and a

multiscale learning paradigm that combines auxiliary

datasets with the extraction of textural and

morphological image features”.

Turning to the CORINE land cover database, the

project inventory was initiated in 1985 (reference

year 1990). Updates have been produced in 2000,

2006, 2012, and 2018. It consists of an inventory of

land cover in 44 classes. The CORINE uses a

Minimum Mapping Unit (MMU) of 25 hectares (ha)

for areal phenomena and a minimum width of 100 m

for linear phenomena. The CORINE is produced by

the majority of countries by visual interpretation of

high resolution satellite imagery. In a few countries

semi-automatic solutions are applied, using national

in-situ data, satellite image processing, GIS

integration and generalisation (CORINE Land Cover

homepage). The CORINE is recognized by decision-

makers as an essential reference dataset for spatial

and territorial analysis on different territorial levels

(Büttner, 2002).To provide more information about

the methodology used for the final products of these

two databases, as follows in Table 1.

In this study the built-up areas data extracted from

very high-resolution images will provide unique

understanding in the mapping of urban expansion and

further the knowledge of human signatures at local

levels. Therefore, we evaluated a set of two datasets,

Table 1: Conceptual Frameworks for overview of COTINE and GHSL databases (CORINE Land Cover homepage, GHSL -

Global Human Settlement Layer homepage).

Subjects CORINE LAND COVER PROGRAMME GHSL PROGRAMME

Classification

of Urban area

The artificial surfaces shape the urban areas main

classes of which are Continuous urban fabric,

Discontinuous urban fabric, Industrial or

commercial units, Port areas, Airports, Mineral

extraction sites, Dump sites, Construction sites,

Road and rail networks and associated lands,

Green urban areas and Sport and leisure facilities.

The built-up area class is defined as the union of all the

spatial units collected by the specific sensor and

containing a building or part of it. Buildings are

enclosed constructions above ground which are

intended or used for the shelter of humans, animals,

things or for the production of economic goods and that

refer to any structure constructed or erected on its site.

Satellite data

and sensor

used

CORINE Land Cover from Copernicus Land

Monitoring Service of European Nations Website

for years 1990, 2000, 2006, 2012 and 2018.

The RS data used are the Landsat programme, which

used of 32808 scenes organized in four collections

corresponding to the epochs 1975, 1990, 2000, and

2014.

Scale The scale chosen for the project was 1:100 000

The surface area of the smallest unit mapped in the

project is 25 hectares.

The scale chosen for the project was 1:50 000

The capacity to discriminate built-up areas was

demonstrated with optical sensors in the spatial

resolution ran

e of 0.5m-10m.

Innovation At Community level, in the CORINE system,

information on land cover and changing land cover

is directly useful for determining and implementing

environment policy and can be used with other data

(on climate, inclines, soil, etc.) to make complex

assessments

(

. ma

erosion risks

)

The new method generalizes the single-variable

single-training set optimization techniques in the

machine learning phase, to the scenario where the

combination of multiple variables in input are taken

into consideration with a combination of multiple

trainin

set collections.

Access to the

data

Free access for all users Fully open and free data and methods access

Coordinate

stems

EPSG:3035, ETRS89 / LAEA Europe Spherical Mercator (EPSG:3857), World Mollweide

(

EPSG:54009

)

Temporal

extent

CLC1990, CLC2000, CLC2006, CLC2012,

CLC2018

1975-1990-2000-2014

Spatial extent Europe:

1990: 26 (27 with late implementation) countries

2000: 30 (35 with late implementation) countries

2006: 38 countries

2012: 39 countries

2018: 39 countries

Global



GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

144

consisting of GHSL alongside CORINE land cover.

While GHSL dataset is available as global coverage,

CORINE land cover dataset is a continental program

for Europe. The reason for selecting these datasets

relies on the fact that they represent in temporal and

in many timespans which allowed extracting urban

expansion in our case studies. The analysis was

carried out over a subset of these datasets in ArcGIS

environment and the data of GHSL- Built-Up Grid

were extracted from GEE platform.

2 METHODS

2.1 Study Area

Estonia is located in Northern Europe, on the eastern

coast of the Baltic Sea. Estonia’s neighbours are

Russia in the East, Latvia in the South, Sweden in the

West and Finland in the North. Its land border is 645

km long, with half of it running along rivers and lakes.

It lies between 57.3 and 59.5 latitude and 21.5 and

28.1 longitude (ESTONICA; Encyclopaedia about

Estonia). In this study we selected two major counties

of Estonia (Figure 1). Harju County which is located

in northern Estonia and the capital and largest city of

Estonia, Tallinn, is situated there. Tartu County

which is located in eastern Estonia and covers 6.9%

of its territory, whereas the city of Tartu is the centre

of the county located at a distance of 186 km from

Tallinn.

Figure 1: Study area.

2.2 Data and Data Processing

In this research spatial data includes

raster format of GHSL, Built-Up Grid 1975-1990-

2000-2015 (P2016) with Image ID:

JRC/GHSL/P2016/BUILT_LDSMT_GLOBE_V1 at

GEE platform code editor and raster data of CORINE

land cover dataset from Copernicus Land Monitoring

Service of European Nations Website (CORINE

Land Cover webpage). GIS vector data was county

boundary shapefiles obtained from Estonian Land

Board Geoportal of Estonia (Maa-amet webpage).

To employ geostatistical analysis methods to

compare and determine the urban expansion,

projection conversion and resolution resetting were

performed. The coordinate system of two datasets

was projected to Lambert Conformal Conic

(Estonia_1997_Estonia_National_Grid) provided by

the World Geodetic System 1984 (WGS84) reference

system. The spatial resolutions of these two datasets

were unified after the grid unit was determined and

data were prepared in the same grid unit size.

Raster data of CORINE land cover were 100-

meter resolution which was resampled to 30 meters’

resolution using nearest-neighbour interpolation to

match the pixel size of both extracted databases. GIS

(ArcMap from the products of ArcGIS Desktop 10.6)

was used to generate and analyze the data. We also

used built-up areas as the “artificial surface” layers of

CORINE land cover database (Table 2).

Table 2: The vector data from CORINE land cover database

reclass to built-up in this study.

CORINE

class

Reclass

Name

CORINE

code

Land cover names

Artificial

Surfaces

Built-

111

Continuous urban

fabric

112

Discontinuous

urban fabric

121

Industrial or

commercial units

123 Port areas

124 Airports

131

Mineral extraction

sites

132 Dump sites

133 Construction sites

122

Road and rail

networks and

associated land

141 Green urban areas

142

Sport and leisure

facilities

2.3 Data Analysis

2.3.1 Spatial Distribution of Built-up Areas

using Annual Growth Rate (AGR)

To monitor the spatial distribution of urban expansion

density, in this research we used two indicators, one

A Comparative Analysis of “Urban Expansion” using Remotely Sensed Data of CORINE Land Cover and Global Human Settlement Layer

in Estonia

145

was annual growth of urban land (AGU: equation 1)

to quantify the increasing urban land areas every year

and the other one was annual growth rate of urban

land (AGR: equation 2) to quantify the urban land's

growth rate every year (He et al., 2017). These two

indicators’ calculating formulas can be expressed as,

𝐴𝐺𝑈  𝑈𝑟



𝑈𝑟



/𝑛 (1)

Where, 𝑈𝑟



and 𝑈𝑟



are the urban land area in

year 𝑡𝑛 and 𝑡 and 𝑛 is the interval of the

calculating period (in years). Annual growth rate of

urban land can be calculated as:

𝐴𝐺𝑅  













 1  100% (2)

Where, 𝑈𝑟



and 𝑈𝑟



are the urban land area in

year 𝑡𝑛 and 𝑡, respectively. Generally, the target

calculating unit is set to the administrative districts of

two counties.

2.3.2 Built-up Density Analysis

Following Sabo et al., (2018), the analysis of built-up

density in a grid as cell size analysis; we used a cell

size of 30×30 m. It is suitable for understanding the

capabilities of mapping of GHSL and CORINE land

cover built-up density and their spatial characteristics.

Thus to derive the built-up density, the total sum of

built-up pixels in a cell is divided by the total area of

the cell. The formula is as follows in equation 3:

𝑑𝑒𝑛𝑠





∑

















(3)

Where 𝑑𝑒𝑛𝑠



is the density for cell 𝑖, 𝑏𝑢



is the

kth built‐up pixel in the specific cell 𝑖, N is the

maximum number of built‐up pixels in one cell, 𝑤



and ℎ



are the width and height of the cell,

respectively.

3 RESULTS

3.1 Results of Spatial Distribution of

Built-up Areas

Table 3 displays AGU and AGR for the Tartu County

and the Harju County. The results of the two datasets

of GHSL and CORINE land cover showed that the

most common trend in the built-up area was the

consistent increase during the years. The area of built-

up based on GHSL database in the Harju County

increased from 7415.73 ha in 1990 to a peak of

8741.16 ha in 2014 which forms average annual

growth of 54.69 ha between 1990 and 2000 and 55.61

ha between 2000 and 2014. Similarly, there was an

increase in built-up area from 22710.96 ha in 1990 to

29879.19 ha in 2012 and 30480.57 ha in 2018 which

means 1.15% annual growth rate between 1990 and

2000 and 1.90% from 2012 to 2018. It was also

apparent trend in Tartu County.

The area built-up extracted from GHSL database

in Tartu County slightly grow from 1299.15 ha in

1990 to 1583.91 ha in 2014 with a smaller annual

growth rate of 0.44% from 2000 to 2014 compared to

AGR growth (0.59%) between 1990 to 2000.

Likewise, the built-up area extracted from CORINE

land cover database showed a rise from 6988.23 ha in

1990 and 7981.65 ha in 2012 and 8353.17 in 2018.

Respectively the annual growth was in the highest

level between 2000 and 2012 at 80.40 ha and the

annual growth of rate was in its peak at 1.68%

between 2012 and 2018.

Table 3: Annual Growth Of Urban Land and Annual Growth Rate in Harju County and Tartu County.

AREA OF BUILT-

UP/AGU/AGR

AREA OF BUILT-UP(ha) AGU(ha) AGR (%)

1990

2000

2012/

2014

2018

1990-

2000

2000-

2012/

2014

2012-

2018

1990-

2000

2000-

2012/

2014

2012-

2018

Harju

County

GHSL 7415 7962 8741

54 55

0.87 0.60

CORINE 22710 24814 29879 30480 210 422 100 1.15 1.03 1.90

Tartu

County

GHSL 1299 1403 1583

10 12

0.59 0.44

CORINE 6988 7016 7981 8353 2 80 61 0.39 0.77 1.68

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

146



Figure 2: Built-up areas in Harju County extracted from

GHSL databases.



Figure 3: Built-up areas in Harju County extracted from

CORINE databases.

Figure 2, 3, 4 and 5 show the built-up area in both

counties extracted from GHSL database and

CORINE land cover database. Figure 2 and 3

represent the urban expansion in constant rings of

5km buffer from Tallinn. While the expansion

happened mostly in the third ring (15km) based on the

GHSL, the expansion of built-up areas significantly

occurred in the 8

ring (40km) from Tallinn.

Respectively the expansion of built-up area in

Tartu County took place mostly in the first ring (5km)

from Tartu based on GHSL database (Figure 4) and

continued in the 6

ring (30km) from Tartu based on

the CORINE land cover database (Figure 5).



Figure 4: Built-up areas in Tartu County extracted from

GHSL databases.



Figure 5: Built-up areas in Tartu County extracted from

CORINE databases.

3.2 Built-up Density Analysis Results

The results of the built-up area density analysis are

presented in Table 4 and Figures 6 and 7. The

densities are calculated per grid cells of 30×30m for

both counties. Regarding the ratio of built-up cells in

Harju County, the GHSL showed a rise from 82397

built-up cells to 97124 cells. The cell density also

increased from 0.017 (1.7%) to 0.020 (2.0%) from

year 1990 to 2014. Respectively the CORINE built-

up cells increased from 252344 to 338673 cells from

1990 to 2018 which expressed a density of 0.052

(5.2%) in 1990 developed to 0.070 (7.0%) in 2018.



A Comparative Analysis of “Urban Expansion” using Remotely Sensed Data of CORINE Land Cover and Global Human Settlement Layer

in Estonia

147

Table 4: Cell Density Analysis Results.

Cell Density

Analysis

CELL

BUILT-

AREA

BUILT-

UP(ha)

CELL

BUILT-

AREA

BUILT-

UP(ha)

CELL

BUILT-

AREA

BUILT-

UP(ha)

CELL

BUILT-

AREA

BUILT-

UP(ha)

TO 1990 2000 2012/2014 2018

Harju

County

GHSL 82397 7415 88474 7962 97124 8741

CORINE 252344 22710 275718 24814 331991 29879 338673 30480

Tartu

County

GHSL 14435 1299 15599 1403 17599 1583

CORINE 77647 6988 77965 7016 88685 7981 92813 8353

Similar growth pattern in Tartu County was

found. Based on the GHSL the density of built-up

cells was 0.3% in 1990 raised to 0.5 % in 2014.

According to CORINE there was a rise in cell density

from 2.1% in 1990 to 2.5% in 2018.



Figure 6: Built-up Cell Density (in %) in Harju County.

Figure 7: Built-up Cell Density (in %) in Tartu County.

4 DISCUSSION

The results provide evidence that the general trend of

urban expansion slightly increased in the built-up area

from 1990–2014 (extracted maps from GHSL-built-

up layer database) and from 1990- 2018 (extracted

maps from CORINE land cover database) in both

counties (Figures 8, 9, 10 and 11).

In terms of spatial distribution of built-up areas,

the two indicators of AGU and AGR were used.

While these two indicators showed increase in built-

up areas and its rate, there was a clear difference

between the data extracted from GHSL and CORINE

land cover databases.

The largest difference was related to AGU in the

Harju County from 2000 - 2014 (GHSL) / 2012

(CORINE). While 55.61 hectares added to urban

lands based on the GHSL, data of CORINE showed

increase by 422.05 hectares. Although the data

extracted from the GHSL showed less built-up areas

as compared to the CORINE during the study period,

there was a higher degree of AGU and AGR in the

GHSL in Tartu County between 1990 and 2000.

There are two main reasons for these differences.

First, it could be the difference in definition and

classification of built-up area by these two datasets.

In line with the ideas of Florczyk et al., (2016), in this

study the “artificial surface” layers of CORINE land

cover database were classified to built-up. These

consist of continuous and discontinuous urban fabric,

industrial or commercial units, port and airport areas,

construction sites, roads and even green urban areas

which are tightly related to built-up structures.

While in GHSL, as Pesaresi et al., (2013) have

mentioned, the built-up area consisted of buildings

and built-up areas. GHSL definition was based on

INSPIRE (Infrastructure for Spatial Information in

Europe) definition for buildings, but did not take into

accounts the underground buildings. Also

classification schema of the GHSL is more general

not assuming any embedded urban/rural dichotomy

(Pesaresi et al., 2009).

1.71%

1.84%

2.02%

5.25%

5.74%

6.91%

7.04%

TO 1990 2000 2012/2014 2018

Harju County GHSL

Harju County CORINE

0.39%

0.42%

0.47%

2.09%

2.38%

2.49%

TO 1990 2000 2012/2014 2018

Tartu County GHSL

Tartu County CORINE

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

148



Figure 8: Urban extend maps of Harju County from GHSL.



Figure 9: Urban extend maps of Harju County from

CORINE.



Figure 10: Urban extend maps of Tartu County fromGHSL.



Figure 11: Urban extend maps of Tartu County from

CORINE.

The results of Figures 12 and 13 also represented

the urban expansion in concentric rings from the

cities of Tallinn and Tartu. While the results of urban

expansion indicated that the GHSL-built-up were

mostly in the third ring (15km) in the Harju County,

the expansion of built-up areas occurred even in the

ring (40km) from Tallinn. Respectively the results

of expansion of built-up in Tartu County showed

similar differences. Based on GHSL database the

expansion took place in the first ring (5km) from

Tartu while CORINE land cover database showed the

expansion in the 6

rings (30km) from Tartu.



Figure 12: Map of Built-up area difference between GHSL

and CORINE in Harju County.

Figure 13: Map of difference between GHSL and CORINE

in Tartu County.

5 CONCLUSIONS

This work presents spatial data extracted from two

important databases of CORINE land cover and

GHSL; Built-Up Grid to determine urban expansion

in two major cities of Estonia. We used temporal data

of the GHSL for 1975, 1990, 2000, and 2014 and

timespans of the CORINE data was 1990, 2000, 2012

and 2018 and the analysis was based on the last

product (2014 for GHSL and 2018 for CORINE). We

analysed the built-up areas and cells which represent

the physical dimension of human settlements and

definitely define the urban expansion of cities using

AGU, AGR and built-up density analysis.

A Comparative Analysis of “Urban Expansion” using Remotely Sensed Data of CORINE Land Cover and Global Human Settlement Layer

in Estonia

149

The results indicated the increase in the amounts

of built-up areas and its rate while in these two

databases the results were not similar in areas and

cells but similar in rate and growth patterns. We

demonstrated that the differences could be due to the

definition of built-up or as explained in conceptual

frameworks of CORINE and GHSL databases, it

could be the results of different satellite data and

sensor used for the final products, the scale of the two

datasets and the temporal extent of the data.

ACKNOWLEDGMENT

The research has been supported by the Estonian

Research Foundation grant PRG352.

REFERENCES

Cheriyadat, A., Bright, E., Potere, D., & Bhaduri, B. (2007).

Mapping of settlements in high-resolution satellite

imagery using high performance computing.

GeoJournal, 69(1-2), 119–129. doi: 10.1007/s10708-

007-9101-0

CORINE Land Cover. (2019, June 14). https://

land.copernicus.eu/pan-european/corine-land-cover

Defries, R. S., & Townshend, J. R. G. (1994). NDVI-

derived land cover classifications at a global scale.

International Journal of Remote Sensing, 15(17),

3567–3586. doi: 10.1080/01431169408954345

Florczyk, A. J., Ferri, S., Syrris, V., Kemper, T., Halkia, M.,

Soille, P., & Pesaresi, M. (2016). A New European

Settlement Map From Optical Remotely Sensed Data.

IEEE Journal of Selected Topics in Applied Earth

Observations and Remote Sensing, 9(5), 1978–1992.

doi: 10.1109/jstars.2015.2485662

Fritz, S., See, L., & Rembold, F. (2010). Comparison of

global and regional land cover maps with statistical

information for the agricultural domain in Africa.

International Journal of Remote Sensing, 31(9), 2237–

2256. doi: 10.1080/01431160902946598

GHSL - Global Human Settlement Layer. (2016, July 6).

Retrieved from https://ghsl.jrc.ec.europa.eu/

He, C., Li, J., Zhang, X., Liu, Z., & Zhang, D. (2017). Will

rapid urban expansion in the drylands of northern China

continue: A scenario analysis based on the Land Use

Scenario Dynamics-urban model and the Shared

Socioeconomic Pathways. Journal of Cleaner

Production, 165, 57–69. doi:

10.1016/j.jclepro.2017.07.018

Herold, M., Scepan, J., & Clarke, K. C. (2002). The Use of

Remote Sensing and Landscape Metrics to Describe

Structures and Changes in Urban Land Uses.

Environment and Planning A: Economy and Space,

34(8), 1443–1458. doi: 10.1068/a3496

Jensen, J. R. and Cowen, D. C. (1999). Remote sensing of

urban/suburban infrastructure and socioeconomic

attributes. Photogrammetric Engineering and Remote

Sensing, 65, 611–622.

Maa-amet. (n.d.). Avaleht. Retrieved from https://geo

portaal.maaamet.ee/eng/Spatial-Data/Administrative-

and-Settlement-Division-p312.html

Michishita, R., Jiang, Z., & Xu, B. (2012). Monitoring two

decades of urbanization in the Poyang Lake area, China

through spectral unmixing. Remote Sensing of

Environment, 117, 3–18. doi: 10.1016/j.rse.2011.

06.021

Patlolla, D. R., Bright, E. A., Weaver, J. E., & Cheriyadat,

A. M. (2012). Accelerating satellite image based large-

scale settlement detection with GPU. Proceedings of

the 1st ACM SIGSPATIAL International Workshop on

Analytics for Big Geospatial Data - BigSpatial 12. doi:

10.1145/2447481.2447487

Pesaresi, M., & Ehrlich, D. (2009). A Methodology to

Quantify Built- Up Structures from Optical VHR

Imagery. Global Mapping of Human Settlement Remote

Sensing Applications Series. doi: 10.1201/9781420

083408-c3

Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Carneiro

Freire Sergio, M., Halkia, S., Julea, A., Kemper, T.,

Soille, P., and Syrris, V. (2016a). Operating procedure

for the production of the Global Human Settlement

Layer from Landsat data of the epochs 1975, 1990,

2000, and 2014 (Publications Office of the European

Union).

Pesaresi, Martino, Ehrlich, Daniele, Ferri, Stefano,

Florczyk, Aneta, Freire, Sergio, Haag, F. Halkia,

Matina, Julea, Andreea, Kemper, Thomas, Soille,

P.,(2015). Global Human Settlement Analysis for

Disaster Risk Reduction, ISPRS - International

Archives of the Photogrammetry, Remote Sensing and

Spatial Information Sciences, doi:10.5194/isprsarchi

ves-XL-7-W3-837-2015

Pesaresi, M., Huadong, G., Blaes, X., Ehrlich, D., Ferri, S.,

Gueguen, L., … Zanchetta, L. (2013). A Global Human

Settlement Layer From Optical HR/VHR RS Data:

Concept and First Results. IEEE Journal of Selected

Topics in Applied Earth Observations and Remote

Sensing, 6(5), 2102–2131. doi: 10.1109/jstars.2013.

2271445

Sabo, F., Corbane, C., Florczyk, A. J., Ferri, S., Pesaresi,

M., & Kemper, T. (2018). Comparison of built‐up area

maps produced within the global human settlement

framework. Transactions in GIS, 22(6), 1406–1436.

doi: 10.1111/tgis.12480

Vijayaraj, V., Bright, E. A., & Bhaduri, B. L. (2007). High

resolution urban feature extraction for global

population mapping using high performance

computing. 2007 IEEE International Geoscience and

Remote Sensing Symposium. doi: 10.1109/igarss.2007.

4422784.

GISTAM 2020 - 6th International Conference on Geographical Information Systems Theory, Applications and Management

150