=Paper=
{{Paper
|id=Vol-1866/invited_paper_2
|storemode=property
|title=Overview of ImageCLEF 2017 Population Estimation (Remote) task
|pdfUrl=https://ceur-ws.org/Vol-1866/invited_paper_2.pdf
|volume=Vol-1866
|authors=Helbert Arenas,Md Bayzidul Islam,Josiane Mothe
|dblpUrl=https://dblp.org/rec/conf/clef/ArenasIM17
}}
==Overview of ImageCLEF 2017 Population Estimation (Remote) task==
https://ceur-ws.org/Vol-1866/invited_paper_2.pdf
Overview of the ImageCLEF 2017 Population
Estimation (Remote) Task
Helbert Arenas (1) , Md Bayzidul Islam (2) , Josiane Mothe (1)
(1) IRIT, UMR5505, CNRS & Université de Toulouse, Toulouse, France
(2) Institute of Geodesy, Faculty of Civil and Environmental Engineering, Technical
University of Darmstadt, Germany
Abstract. Estimating population has many applications as planning
disaster responses, communication infrastructure, or development activ-
ities. In 2017, ImageCLEF Lab introduced a new pilot task: the Popula-
tion Estimation (or Remote) task which aims at estimating the popula-
tion of an area of interest by exploring Copernicus earth observation data
(i.e. free Sentinel-2 satellite images). In line with the goal of FabSpace 2.0
project, a European Unions initiative to bring Geo-Enthusiast together
around the 6 European universities in collaboration with Business Incu-
bation Centers, participated to this challenge. This paper presents the
results that were obtained by the participants as well as a brief summary
of some of the approaches that were used.
Keywords: Information Systems, Information Retrieval, Image Retrieval, Coper-
nicus data, Automatic Population Estimation, FabSpace 2.0, CLEF
1 Introduction
Estimating population for the planning of communication infrastructure and
any development activities is an important issue. Such accurate estimation is
also crucial for NGOs before engaging any rescue operation or humanitarian
action. Population estimation is indeed fundamental to provide any services
for a particular region. While good estimates exist in many parts of the world
through accurate census data, this is usually not the case in developing countries.
Collecting new census is expansive and time consuming; it is not possible in case
where decision has to be taken quickly. Moreover, census data are generally
based on political areas such as cities or counties while humanitarian and rescue
actions and/or communication activities are more geographically-based as they
take place in the areas of catastrophe such as where hurricane, earthquake or
flood happened and to combat epidemic diseases.
Alternatively, counting the number of buildings (given the fact they can
be properly extracted) can provide a first estimate; however, it may not be
enough since people in various places in the globe do not live the same way,
the population may vary in summer and winter in different touristic places, or
population may vary where there is easy access to public services or amenities.
Moreover, the distribution of population in the coast and the urbanized area is
not similar in terms of building structures and spatial distributions, household
sizes and topographical representations.
This pilot task is part of the imageCLEF 2017 Labs [13] and was introduced
this year. It aims at investigating the use of non commercial satellite data as
a free and quicker process to estimate the population of an area of interest.
More specifically, this pilot task aims at exploring Copernicus 1 arth observation
data (i.e. Sentinel-2 satellite images) which are available for free and in a good
temporal and spatial resolution.
2 Population Estimation Pilot Task Overview
In this pilot task, participants had to estimate the population for different areas
in two regions Lusaka and Uganda. To achieve this goal, organizers provided a
set of satellite images (Copernicus Sentinel-2). The boundaries of the areas of
interest were provided as shape files. The clipped satellite images were provided
as well as the meta data of the original images (before clipping). This pilot
task uses Copernicus Sentinel-2 multi-spectral images with 13 bands (resolution
between 10 and 60 meters). However, participants were allowed to use any other
resource they think may help to reach the highest accuracy.
The study area consist of 83 areas of interest in the city of Lusaka and 17
in west Uganda for which the population has to be estimated, for a total of 100
areas. For 90 of these areas ground truth is available thus evaluation considered
these areas only.
This pilot task was initiated within FabSpace 2.0 project2 in collaboration
with CartONG, founded in 2006, CartONG is a French non-governmental or-
ganization committed to furthering the use of geographic information tools to
improve data gathering and analysis for emergency relief and development pro-
grammes around the world. 3 The task is an open challenge in the ImageCLEF
laboratory as well as in the 6 FabSpaces laboratory in Europe.
3 Dataset and evaluation protocol
3.1 Dataset
The data set4 consists of topographic and geographic information as follows:
1
www.copernicus.eu/main/sentinels
2
FabSpace 2.0 is the open-innovation network for geodata-driven innovation by lever-
aging Space data in particular, in Universities 2.0. This project received funding
from the European Unions Horizon 2020 Research and Innovation program under
the Grant Agreement no 693210. More information at https://www.fabspace.eu/
and [6]
3
http://www.cartong.org/fr
4
The dataset is available on Zenodo with the DOI 10.5281/zenodo.804602 or on de-
mand
– ESRI shape files: there is single shape file by region and the projected shape
file of the region has the necessary attributes to represent the various areas
the region is composed of.
– Sentinel-2 satellite images: The remote sensing imagery comes from the
Sentinel-2 platform. The imagery is multi spectral, cloud-free satellite im-
agery downloaded from Sentinel Data Hub5 . The images have been clipped to
match the bounding box of the areas of interest. The bands for images have
different spatial resolutions: 10 meters for bands B2 (490nm), B3 (560nm)
B4 (665 nm) and B8 (84nm); 20 meters for bands B5 (705nm), B6 (749nm)
B7 (783nm), B8a (865nm) B11 (1610nm) and B12 (2190nm). For the anal-
ysis, participants were encouraged to use Red, Green and Blue bands or in
some cases near infrared bands which are 10 meters resolution.
– Meta-data associated to the images: Information regarding the original im-
ages is provided in XML files. These files contain information like capture
time/date, sensor mode, orbit number, the id of quality files, etc. Further
information regarding the Sentinel-2 products, as well as file structure can
be found in the Sentinel 2 User handbook6 .
In this pilot task two regions has been selected: city of Lusaka and West of
Uganda. More specifically, the data set is composed of the following structure:
– City of Lusaka: The subareas are based on Operational Divisions, a unit
defined by Médecins Sans Frontières in 2016. This organization divided the
city of Lusaka in 83 units. For this region, the data set consists of: (1) ESRI
shape file including locational and attributes information, (2) Two Sentinel-
2 level-1C satellite images covering the area and for each image there are all
13 bands, (3) XML meta data associated to image files.
– West Uganda: In Uganda there is 17 subdivisions and for this region data
sets consists of: (1) ESRI shape file including locational and attributes in-
formation, (2) Five Sentinel 2 level-1C satellite images covering the area and
for each image there is all necessary bands, (3) XML meta data associated
to image files.
The images from the data set are stored in zipped files with the following
folder structure:
5
https://scihub.copernicus.eu/dhus/#/home
6
https://sentinel.esa.int/documents/247904/685211/Sentinel-2 User Handbook
[NAME OF THE STUDY REGION]: Lusaka or Uganda.
[shp]: This folder contains a shapefile with the boundaries of the study
areas.
[sentinel2]
[ID OF SATELLITE IMAGE]: Original id of the image as in the Sen-
tinel Data Hub.
[bands]: This folder contains the bands of the image. Each band is a Geo-
Tiff file. Each band corresponds to a certain electromagnetic spectrum
captured by the sensor.
[xml]: This folder contains the XML files that contains metadata in-
formation regarding the images.The information applies to the original
source (before image clipping). By using the information in this file a
user can obtain the original dataset.
3.2 Evaluation
The evaluation method uses three metrics that compare the predicted values
and the ground truth s: 1) Sum of the differences over the areas, 2) Root Mean
Square Error, and 3) Pearson correlation.
The challenge comprise of geographically separated two areas, West Uganda,
and Lusaka in Zambia. Because of this fact, it was decided to evaluate both areas
separately but together in a single file. We evaluate the results considering two
variables: 1) Population counts, and 2) Dwelling counts. Each run submitted by
the participants has 12 possible metrics. However, not all the participants sub-
mitted results for both variables. All the submissions provided were estimation
of the population, while only two of them provided estimates for both population
and dwelling units.
The next subsections will provide a short description of the metrics used to
evaluate the results.
Sum of differences This evaluation criteria estimates the differences between
the predicted value and the ground truth. For each operational zone, we calculate
the absolute value of this difference and summing up for the whole areas. The
equation used is:
n
X
SumDelta = |(dt − st )|
t=1
Where n represents the total number of operational zones to evaluate, dt
represents the ground truth value, and st represents the estimation produced by
the participant for the operational zone t.
Root Mean Square Deviation Root Mean Square Error (RMSE) (also known
as Root Mean Square Deviation) is one of the most widely used statistics to
compute the differences between a modeled output and the observed values.
The individual differences between the produced estimation and ground truth
and/or the standard deviation of residuals are called prediction errors (dt −st ). If
the ground truth is dt and the estimated values is st are standardized (resulting
in zero means and unit standard deviation) then the RMSE between dt and st
can be calculated by following equation [4].
r Pn
2
t=1 (dt − st )
RM SE =
n
The variables depicted in this equation are the same as the one previously
described in the previous section for the equation Sum of differences.
Pearson Correlation this evaluation process calculates the Pearson correla-
tion between the run estimates and ground truth values. Pearson correlation is
between +1 and −1, where +1 represents absolute positive linear correlation, 0
represents no correlation, and −1 represents negative correlation [17].
Pn Pn
t=1 (dt − d) t=1 (st − s)
r = qP
n n
pP
t=1 (dt − d) t=1 (st − s)
AvgRelDelta We considered an additional measure we called the average of
the relative deltas which is defined as follows/
Let us consider the relative delta as (dtd−s
t
t)
. The range of values is from
−∞ to 1. Negative values indicate overestimation, while positive values indicate
underestimation.
We can then calculate the relative delta in percentage as (dtd−s
t
t)
∗ 100
The range of values would be −∞ to 100. This measure will be used for
the maps depicting the results in the next section. We can calculate this value
at the operational zone level. In this way we can indicate in which operational
zone the proposed algorithm failed, how much and in which way (overestima-
tion/underestimation).
However, to obtain an overall estimation for all the operational zones, it
is necessary to use the absolute value of the relative deltas as |(dtd−s t
t )|
∗ 100.
Otherwise, the delta values could cancel among themselves.
Then we obtain an average of the relative deltas which is defined as:
Pn |(dt −st )|
t=1 dt ∗ 100
AvgRelDelta =
n
where n is the number of operational zones considered for the estimation.
The lower the value of AvgRelDelta the more accurate the approach is.
4 Participants and results
Although the pilot task was opened to anyone, the actual participants came
from local events (i.e. Hackathon, Idea contest etc.) that were organized within
FabSpace 2.0 project in the 6 local FabSpace laboratories. Although different
teams started working on the pilot task, four teams only managed to send a run
(See Table 1 in the next section). Details can also be found in [2].
The methodologies and the background of the members of the participating
teams was heterogeneous. For instance, in the case of Italy, the participating
team was composed of two students from the university Tor Vergata, one is a
Management Engineering student while the second one is a Computer Science
Engineering student [18]. In the case of Greece, the background of the partic-
ipants included domains such as electrical and mechanical engineering as well
as business studies [15]. From Germany three participants participated where
one is a graduate student of Environmental Engineering, second is a student of
Physical Geography and the third one is a PHD student [14]. Also a couple of
participants from diverse background were participated at Poland.
The approaches used were also diverse. In the case of Italy, the participants
used an approach based on a convolutional neural network (CNN) that makes
use of Sentinel-2 Imagery as well as built up estimates provided by the Cen-
ter for International Earth Science Information Network (CIESIN) [18]. While
in the case of Germany, the approach was based on supervised and unsuper-
vised image classification with the inclusion of not only optical but also radar
imagery [14]. The classification also includes supervised as Minimum Distance
and Maximum Likelihood Classification algorithm and unsupervised as K-Means
Cluster analysis algorithm. To run the classification algorithm different software
as QGIS - SCP plugins, Sentinel Toolbox and Python platform have been used.
The team from Greece used satellite imagery based on classification techniques
coupled with a statistical forecast on historical data [15]. At the Poland partici-
pants used Railways approach to estimate the number of buildings using radar
imagery i.e. Sentinel-1 images [2].
4.1 Results
Table 1 presents the results for the population estimation for the 4 official runs
from participants, using the 4 measures presented above and both on the indi-
vidual regions (Uganda and Zambia) and overall. Table 2 presents the results
for the house estimation for the two runs using the same measures.
The best results estimating the population for one particular geographic
area (Uganda or Zambia) were submitted by the Greek team for Uganda (Av-
gRelDelta:38.75). However, their approach failed for the region of Lusaka (Av-
gRelDelta:Zambia), giving them an overal (AvgRelDelta:177.55). The Italian
team provided the second best result (AvgRelDelta:50.5), also for the region of
Uganda. While, their approach provided fair results for the region of Lusaka (Av-
gRelDelta:96.31), resulting in the best overall approach (AvgRelDelta:87.57). If
we consider the values of Sum Delta and RMSE the Italian team has also the
best values.
In the case of the Pearson value, in general the submitted results did not
resulted in high overall values. They only obtained high values for the region of
Table 1: Population estimation. UGD stands for Uganda while ZMB is for Zam-
bia region. Overall is when considering both regions all together. Bold font high-
lights the best value and italic the best second value.
Participant Country Geographic Zone Sum Delta RMSE Pearson AvgRelDelta
Darmstadt Germany UGD 26,755 2,199 0.87 115.93
Darmstadt Germany ZMB 1,466,397 30,510 0.11 93.69
Darmstadt Germany Overall 1,493,152 27,495 0.22 97.89
Grapes Greece UGD 10,160 770 0.95 38.75
Grapes Greece ZMB 1,476,753 38,072 0.25 209.87
Grapes Greece Overall 1,486,913 34,290 0.33 177.55
AndreaDavid Italy UGD 18,485 1,816 0.76 50.05
AndreaDavid Italy ZMB 1,465,603 30,480 0.08 96.31
AndreaDavid Italy Overall 1,484,088 2,7462 0.21 87.57
FABSPACE PL Poland UGD 22,730 2,103 0.11 115.99
FABSPACE PL Poland ZMB 1,535,909 35,294 0.29 186.08
FABSPACE PL Poland Overall 1,558,639 31,799 0.37 172.84
Participant Country Geographic Zone Sum Delta RMSE Pearson AvgRelDelta
Darmstadt Germany UGD 7,272 638 0.87 146.37
Darmstadt Germany ZMB 290,197 6,055 0.11 104.71
Darmstadt Germany Overal Houses 297,469 5,460 0.22 112.58
FABSPACE PL Poland UGD 4,546 420 0.11 115.99
FABSPACE PL Poland ZMB 270,007 5,494 0.44 100.58
FABSPACE PL Poland Overal Houses 274,553 4,952.17 0.51 103.49
Uganda. Again the best value was obtained by Greece (Pearson:0.95), the second
best value was of the German team (Pearson:0.87).
Considering the AvgRelDelta we can say that in general the best population
estimation approach is the one submitted by the Italian team (AvgRelDelta:
87.57).
4.2 Summary of participants’ approaches
The aim of this work was to estimate the population for selected areas in Uganda
and Zambia with Sentinel-2 data.
The approaches used by the participants to tackle this challenge were diverse.
In the case of Italy, the participants used an approach based on a convo-
lutional neural network (CNN) that makes use of Sentinel 2 Imagery (bands
2,3,4,8) as well as LDS built up estimates provided by the Center for Interna-
tional Earth Science Information Network (CIESIN). The LDS Built up esti-
mates have a resolution of 30m and provide build up information as recent as
2014. The most recent estimates are based on Landsat 8.
In the case of Germany, the approach was based on diverse techniques of
image classification. The participants used a subset of the bands provided by
Sentinel 2 (bands 2,3,4 and 8) and also tested using only near infra-red band.
The classification techniques used were supervised and unsupervised, using tools
as QGIS and Sentinel Tool Box (SNAP). As part of the approach, additional
information from Sentinel 1 radar imagery was used.
In the case of Poland Railways approach to the number of buildings was done
using radar images. The results obtained by estimating the number of buildings
from radar imagery was more applicable. Optical data has been used to divide
areas by type of building. The results obtained and their comparison to the
number of buildings was very promising.
4.3 More evaluation
Fig. 1. Overview of the results submitted by the participating teams Team for the
operational zones in Lusaka- Zambia.
Figure 1 depicts an overview of the results submitted by the participating
teams. The maps depict the prediction errors divided by the ground truth pop-
ulation for each operational zone. We divide the prediction errors in order to
consider the great range of population counts among the operational zones. We
used the relative delta as described in Section 3.2.
Operational zones in which the models have severely overestimated the pop-
ulation (the models suggest more population that the real one) are depicted in
red or orange. We consider severely overestimated results when the population
estimation is more than 50% of the actual population. Areas in which the estima-
tion is +/− 50% are depicted in green, while areas in which the models severely
underestimated the population are depicted in blue. For this paper, a population
estimation would be consider severely underestimated if it is less than 50% of
the actual population.
We can see that there are areas overestimated by all the models: The In-
dustrial area (West), Ngwerere (North) and Libala (South). In the case of the
industrial areas, it seems that the proposed algorithms confused industrial build-
ings for residential areas. In the case of Ngwere, and Libala, the residential areas
have low density which was incorrectly evaluated by the algorithms.
On the other hand, we can see that there are other areas that have been
underestimated by all the models: George, Lilanda, Desai, (at the West of the
city), Chelston at the East, Chawama and Kuoboka at the South.
Most of the models underestimated the population in Makeni, except for the
Polish team. This team also differentiated from the rest in an area comprised by
Ngombe, Chamba Valley, Kamanga and Kaunda square (North East of the city),
providing good estimates with the exception of Chudleigh, which was which was
overestimated by all the teams, except for the Italian team.
In general, all the teams got their best results in an area near the center
of the city, an area roughly defined by the Operational zones, Civic Centre,
Rhodes Park, and in most cases Northmead (except for the Polish team who did
not provide a good result for this zone).
Figure 2 depicts the results of the participating teams for the operational
zones in Uganda. As in the map depicting the operational zones located in
Lusaka, the map depicts the zones with severe overestimation in red and or-
ange (less than -50%), while it depicts the areas with severe underestimation in
blue (more than 50%).
In the case of German team, they overestimated 5 of the 17 operational zones
(depicted in orange and red) and underestimated 7 of the zones depicted in blue.
The solution proposed by the Greek team has less overestimation, only one of the
zones was overestimated. However, it severely underestimates 5 zones, while it
has a good estimation for 11 zones. The submission of the Italian team, does not
have any overestimation. However, it severely underestimates the population in 9
of the zones. The approach followed by the Polish team, produces mixed results.
It severely underestimates the population in 5 zones while it underestimates it
in 6.
5 Related work
Emerging technologies including mobile application, global positioning system
and satellite images can create changes in development. Estimation of popula-
tion using earth observation and geospatial technologies can ease emergency ser-
Fig. 2. Overview of the results submitted by the participating teams, for the opera-
tional zones in Uganda.
vices and humanitarian actions. For instance, providing emergency relief, tack-
ling post-earthquake situation and tracking epidemic diseases like cholera needs
up-to-date and geographically specific information. The accurate information a
governmental organization and/or NGOs and/or other aided organization can
have, the more they can save lives.
A couple of related works has been done to estimate population and make it
atomize to avoid unnecessary expanses and time required in conventional cen-
sus methods of estimating population. In their studies Florence A. Galeon [10]
and Almeida et al. [1] underline the potentials of using high resolution Quick-
bird images to estimate population in informal and semi-informal settlements in
urban area. In another study, United Nations Statistics Division DESA [9] in-
tegrated GPS, digital imagery and GIS technology to make the census mapping
more accurate and efficient. Li and Weng [16] integrated Landsat ETM+ data
with census data for estimation of population density of Indianapolis, Indiana
USA. They incorporated spectral signatures, principle components, vegetation
indices, fraction images, textures and temperature as predictive indicators to
make a correlation analysis between remote sensing parameters and population.
In the studies of Cheng et al. [5], Prosperie, [19] and Sutton [21] [22], the au-
thors used “night satellite” imagery to estimate population according to the local
densities of light sources. Dittakan et al. ’s studies [7] [8] used satellite images
from GeoEye 50m ground resolution data publicly available by Google Earth.In
another studies Ilka et al. [20] used TM/Landsat5 and ETM+/Landsat7 data
to estimate population of Belo Horizonte city in Brazil by generating statistical
model.
There are also plenty of other studies that used high to moderate resolution
satellite imagery to estimate population. There are relatively few studies on esti-
mating population based on radar satellite data such as Henderson, F. and Xia,
Z. elaborated radar applications in Urban Analysis, settlement detection and
population estimation [11] [12], Balz, T. and Haala, N., interpreted high reso-
lution SAR data using existing GIS data in urban areas to estimate population
[3].
There are lots of other new satellites providing either commercial or free
satellite images which can be also evaluated in the task of estimating the pop-
ulation of an area. However, Sentinel-2 optical and Sentinel-1 radar satellites
are free and moderately high resolution satellite imagery. In this ImageCLEF
2017 pilot task the prime objective was to precisely identify how powerful are
Sentinel-2 imagery of 10m resolution.
6 Conclusion
This paper summarizes the pilot task introduced this year at image CLEF 2017
and that aims at estimating the population of a zone. Participants encountered
various difficulties mainly linked to the nature of the images they had to use.
The results that were obtained show that there is room for improvement using
low resolution images for estimating the population in a zone although these
images have the huge advantage of being free of use.
7 Acknowledgement
This project received funding from the European Unions Horizon 2020 Research
and Innovation programme under the Grant Agreement n693210. https://www.fabspace.eu/
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