International Journal of Geosciences, 2013, 4, 6-13 Published Online August 2013 (
Mapping Cropland in Ethiopia Using Crowdsourcing
Linda See1, Ian McCallum1, Steffen Fritz1, Christoph Perger1, Florian Kraxner1,
Michael Obersteiner1, Ujjal Deka Baruah2, Nitashree Mili3, Nripen Ram Kalita4
1Ecosystems Services and Management Program, International Institute for Applied Systems Analysis, Laxenburg, Austria
2Department of Geography, Gauhati University, Guwahati, India
3Centre for Geographical Studies, Dibrugarh University, Dibrugarh, India
4Department of Geography, B. Borooah College, Guwahati, India
Received June 15, 2013; revised July 15, 2013; accepted August 8, 2013
Copyright © 2013 Linda See et al. This is an open access article distributed under the Creative Commons Attribution License, which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The spatial distribution of cropland is an important input to many applications including food security monitoring and
economic land use modeling. Global land cover maps derived from remote sensing are one source of cropland but they
are currently not accurate enough in the cropland domain to meet the needs of the user community. Moreover, when
compared with one another, these land cover products show large areas of spatial disagreement, which makes the choice
very difficult regarding which land cover product to use. This paper takes an entirely different approach to mapping
cropland, using crowdsourcing of Google Earth imagery via tools in Geo-Wiki. Using sample data generated by a
crowdsourcing campaign for the collection of the degree of cultivation and settlement in Ethiopia, a cropland map was
created using simple inverse distance weighted interpolation. The map was validated using data from the GOFC-GOLD
validation portal and an independent crowdsourced dataset from Geo-Wiki. The results show that the crowdsourced
cropland map for Ethiopia has a higher overall accuracy than the individual global land cover products for this country.
Such an approach has great potential for mapping cropland in other countries where such data do not currently exist.
Not only is the approach inexpensive but the data can be collected over a very short period of time using an existing
network of volunteers.
Keywords: Cropland Mapping; Crowdsourcing; Interpolation; Validation
1. Introduction
Climate change will have far reaching effects on agricul-
tural production in the future, where many studies have
shown that crop yields, particularly in Africa, will be
compromised under a warmer climate [1,2]. With pres-
sures to increase agricultural production by as much as
70% by 2050 in order to feed the predicted population of
9 billion people [3], global food security issues are of
pressing importance. Monitoring food security and eva-
luating the ability of countries to respond to food short-
ages requires good baseline information on the spatial
distribution of cropland [4], while this spatial informa-
tion is also a critical input to economic land use models
that predict future competition for land across multiple
sectors including agriculture [5].
Information on the spatial distribution of cropland
comes from two main sources. The first is global land
cover maps, which usually have a category for cultivated
and/or managed land as well as mosaics of cropland and
natural vegetation. By extracting these classes from the
global products, it is possible to produce a simple crop
mask. The second source of information is national land
cover maps, which are usually produced by national
mapping or government agencies. However, these maps
are not always available or, in some cases, have not even
been produced, particularly for some developing coun-
tries. For this reason, initiatives such as AFRICOVER
have been developed in which the Food and Agriculture
Organization of the United Nations (FAO) has worked
with member governments in selected countries in Africa
to produce land cover and land use maps at a resolution
of 100 m [6].
Land cover maps are created using a top down ap-
proach in which remotely-sensed data from satellites are
classified. For example, the GLC-2000 (Global Land
Cover 2000) was created from 14 months of SPOT-
VEGETATION at a 1 km resolution for the environ-
mental reference year 2000 [7], while GlobCover 2005,
at a 300 m resolution, was created from the MERIS sen-
opyright © 2013 SciRes. IJG
sor (Medium Resolution Imaging Spectrometer) onboard
the European Space Agency’s Envisat platform, which
stopped operation in 2012 [8]. The MODIS global land
cover product is available at a resolution of 500 m and is
produced by Boston University using data from the Mo-
derate Resolution Imaging Spectroradiometer (MODIS).
All of these products provide comprehensive spatial
coverage but their accuracies in the cropland domain
range from only 58% to 77% [9]. More critically, when
the land cover products are compared with each other,
they show large spatial disagreements in terms of where
the cropland is actually located. Although one would
expect some differences between these products since
they have been created using different sensors, different
classification algorithms and different training and vali-
dation datasets, the uncertainties in the location of crop-
land mean that these products are not accurate enough for
many applications including those related to food se-
curity. This leaves the end users of these products with
an information deficit that needs to be urgently add-
One way in which this gap might be filled is to use an
alternative to the more conventional top down approach
to mapping. Instead of employing automatic and semi-
automatic classification algorithms, it is possible to use
citizens and interested experts to crowdsource this infor-
mation using a bottom up approach. The idea behind
crowdsourcing is to outsource tasks to the crowd, hence
the origin of the term [10], which can involve simple data
collection to more analytical and problem solving
exercises. The Amazon Mechanical Turk is one example
of a crowdsourcing site that allows businesses to out-
source tasks that they are unable to do themselves, pro-
viding very small amounts of compensation to the parti-
cipants [11]. However, crowdsourcing is also used to
refer to data collection and analysis by the crowd where
the efforts can contribute to more social and environ-
mental causes, e.g. the identification of waste dump sites
to initiate cleanup operations or the mapping of critical
features in a post-natural disaster environment [12,13], or
directly involving citizens in scientific research through
citizen science, e.g. classification of galaxies or the iden-
tification of invasive species [14,15]. As many types of
crowdsourced data are georeferenced, the term volunteer-
ed geographic information (VGI) is also used to describe
these types of contributions [16], where OpenStreetMap
is a classic example of community mapping [17].
In the specific area of land cover, a crowdsourcing tool
called Geo-Wiki has been developed for the visualization,
validation and improvement of global land cover using
Google Earth [18,19]. A number of crowdsourcing com-
petitions have been run in which a sample of pixels was
provided to interested citizens and experts, who deter-
mined the type of land cover visible from Google Earth.
To date the crowdsourced data from Geo-Wiki have been
used to validate a global map of land availability for
biofuel production [20] and work is underway to develop
a global hybrid land cover product that integrates exist-
ing land cover maps with the crowdsourced data. How-
ever, it is also possible to directly use the crowdsourced
data to create land cover maps when the density of
samples from Google Earth is high enough. For example,
a large sample was gathered from a previous Geo-Wiki
competition, which was aimed at gathering information
on the degree of cropland and human settlement across
Ethiopia in the context of land acquisitions [21].
The aim of this paper, therefore, is to demonstrate how
crowdsourcing can be used to create a simple map of
cropland for Ethiopia. The results will demonstrate that a
simple data collection exercise can produce a cropland
map with higher accuracy than current global land cover
products for this country in the cropland domain. Ethio-
pia is, in fact, a country where good national level data
on land cover and land use are available through the
AFRICOVER initiative but the data are not openly shar-
ed and are therefore not accessible. This approach has
great potential for other countries where current crop-
land information is either not accurate enough or cur-
rently unavailable due to the data policies of a particular
2. Data
Two main sources of data were used to create the crop-
land map of Ethiopia: 1) data on the degree of cropland
visible from Google Earth, where a sample was crowd-
sourced via a Geo-Wiki competition; and 2) data used to
validate the map, which originate from multiple sources
as explained below.
2.1. Crowdsourced Data on Cropland
USAID (United States Agency for International Devel-
opment) held a Food Security Open Data Challenge
(“Hacking for Hunger”) in the middle of September 2012
where different problems requiring a solution were pre-
sented to the hacking community. The Geo-Wiki team
proposed a challenge calling for individuals to help col-
lect information on cropland and human settlement
across Ethiopia using a simplified version of Geo-Wiki
as shown in Figure 1. The blue box on the interface
represents a 1 km2 pixel, where a random sample of pix-
els was generated across Ethiopia.
Users were asked to examine the Google Earth image
in the 1 km2 area and to indicate the degree of visible set-
tlements and cropland from “none” present to a “high”
degree. Instructions with examples were provided to help
users gain experience in interpreting Google Earth im-
ages. Users were encouraged to contribute as many of
Copyright © 2013 SciRes. IJG
Copyright © 2013 SciRes. IJG
these pixels as possible and to share interesting findings
via facebook.
The idea behind the challenge was to collect evidence
for cross-referencing the crowdsourced information with
data from the Land Matrix ( This
project collects the locations of land acquisitions or “land
grabbing” so the idea was to see whether areas targeted
for land acquisitions are areas of existing cropland and
settlement, where Ethiopia is one of the worst affected
countries [22]. Some evidence of this has been found
[21], which means that population displacement may
occur if these land acquisitions take place, and as a con-
sequence, local livelihoods could be negatively affected.
During the “Hacking for Hunger” event, more than
2000 pixels of 1 km2 were collected. The site was then
opened up to the Geo-Wiki network of volunteers in the
form of a three week competition to collect as much in-
formation as possible. By the end of the three week pe-
riod, more than 77,000 pixels were collected where the
coverage is shown in Figure 2.
Figure 1. Geo-Wiki interface for data collection.
Figure 2. Distribution of crowdsourced data collected for Ethiopia by cropland category.
Information on the degree of cultivation was collected in
four categories: none, low, medium and high. These were
reclassified to numerical values as follows: 0%, 20%,
50% and 90% respectively.
2.2. Data for Map Validation
Validation data were available from the GOFC/GOLD
validation portal [23], which includes data used to vali-
date the GLC-2000, the STEP (System for Terrestrial
Ecosystem Parameterization) database, which is used to
train and validate MODIS land cover, and the Visible
Infrared Imaging Radiometer Suite (VIIRS) database,
which is being developed to validate a new land surface
product. Validation data from the Chinese 30 m land
cover map were also used [24]. These validations are
only at a single point rather than a pixel so they were first
reviewed for homogeneity across a larger area, and those
points which fell in complex landscapes were removed
from the validation exercise. Finally, crowdsourced data
from the first Geo-Wiki competition [25] provided an
independent source of validation data. The validation
data from these different sources were extracted for
Ethiopia and each area was then reviewed using Google
Earth to ensure quality. After data clean-up, there were
493 validation points available for the accuracy assess-
ment (see Section 3.3).
3. Methods
3.1. Interpolation
A simple inverse distance weighted interpolation method
was used to create the Ethiopian cropland map. This in-
terpolation method is based on Tobler’s first law of ge-
ography, i.e. things that are close together are more re-
lated to one another than things further away [26]. For
each grid point to be interpolated, the algorithm identifies
all the other points within a certain neighborhood and
calculates a weighted vector, w, based on a simple in-
verse power function:
wd d
where d is the distance and x governs the rate of distance
decay. A value of 2 is most commonly chosen for x. Each
interpolated point is then calculated as a weighted aver-
age of its neighbors. For this exercise the default values
in ArcGIS were used, i.e. a power of 2 and a neighbor-
hood of 12 points. Although different settings and inter-
polation methods could be employed, the point of this
study was not to optimize the method of interpolation but
rather to demonstrate how even simple interpolation can
effectively be used to create a cropland map based on
3.2. Difference Maps
The crowdsourced cropland map was compared to the
GLC-2000, MODIS and GlobCover where the cropland
classes were extracted and the images were reclassified
to produce maps showing areas with the presence and
absence of cropland in Ethiopia. The GLC-2000, MODIS
and GlobCover were resampled to match the resolution
of the crowdsourced cropland map (i.e. 1 km). The im-
ages were then subtracted to produce difference images
in order to highlight the main areas of disagreement.
3.3. Map Validation
The crowdsourced cropland map was validated using the
dataset described in Section 2.2 by extracting the pres-
ence or absence of cropland at each of the validation lo-
cations. A confusion matrix was then populated (Table 1 )
and the overall accuracy was calculated as follows:
Accuracy 100
where i is the class from the map of interest, e.g. the
crowdsourced cropland map, j is the class from the vali-
dation data set and n is the total number of classes.
In addition, user’s and producer’s accuracies were
calculated at follows:
User’s accuracyi = ,
Producer’s accuracyj = ,
The user’s accuracy reflects errors of commission
while the producer’s accuracy refers to errors of omis-
sion. The same accuracy measures were then applied to
the GLC-2000, MODIS and GlobCover for Ethiopia and
the cropland class using the same validation dataset.
4. Results
The interpolated cropland map for Ethiopia is provided
Table 1. Confusion matrix.
Class from the validation data
Class from the
cropland map Class 1 ... Class n
Class 1 x1,1 ... xn,1
... ... ... ...
Class n xn,1 ... xn,n
Copyright © 2013 SciRes. IJG
in Figure 3 while a difference image between this map
and the GLC-2000 is provided in Figure 4.
The GLC-2000 shows much more cropland than the
intepolated map although there are some areas (shown in
red) where the interpolated map indicates cropland but
the GLC-2000 does not.
Figures 5 and 6 contain images showing the spatial
differences between the interpolated map (Figure 3) and
MODIS and GlobCover respectively. In contrast, MODIS
shows much less cropland than the interpolated map,
missing quite a significant area of cropland in the central
Eastern part of the country known as the Harerghe High-
lands where rainfed agriculture is definitely reported [27].
GlobCover, like the GLC-2000, shows more cropland
than the interpolated map throughout most of the country
but also misses areas in the central part where the inter-
polated map indicates cropland. Such a simple visual
comparison serves to highlight the large spatial differ-
ences between each of the land cover maps and the in-
terpolated cropland map, but it also highlights the differ-
ences among the different products.
Table 2 contains the accuracy measures for the three
Figure 3. Interpolated cropland map of Ethiopia.
Figure 4. Comparison of the interpolated cropland map with the GLC-2000.
Copyright © 2013 SciRes. IJG
L. SEE ET AL. 11
Figure 5. Comparison of the interpolated cropland map with MODIS.
Figure 6. Comparison of the interpolated cropland map with GlobCover.
global land cover products for Ethiopia and the interpo-
lated map. Overall accuracies range between 74.5% for
Globcover to 89.3% for the interpolated map, showing
just under an 8% increase in accuracy over the second
best product, i.e. MODIS. Thus, the map produced
through interpolation of crowdsourced data has the best
overall accuracy.
In terms of user’s accuracy, all the maps have high
values for the category “No crop” but lower values for
the presence of cropland. This indicates that identifica-
tion of areas without cropland is easier than the opposite
case, which is not surprising for the global products since
Table 2. Accuracy assessment.
Accuracy measures (%)
Maps Overall
Crop Crop No
Crop Crop
GLC-2000 77.3 90.5 48.1 79.569.6
MODIS 81.8 83.2 67.5 96.129.3
GlobCover 74.5 89.3 43.9 76.866.3
cropland map89.3 91.7 78.8 94.968.5
Copyright © 2013 SciRes. IJG
they are poor at detecting croplands in areas of low agri-
cultural intensification. This is because the spectral sig-
natures and temporal profiles are similar to grasslands,
which would include areas of Ethiopia. The values for
identification of the “Crop” class in the global land cover
products range from 67.5% for MODIS to 43.9% for
GlobCover. However, from a user’s perspective the in-
terpolated map has the highest accuracy for the presence
of cropland at 78.8%.
For the producer’s accuracy, both MODIS and the in-
terpolated map performed very well in terms of labeling
areas as having “No crop” while the GLC-2000 and
GlobCover performed less well. However, MODIS per-
formed very poorly in falsely labeling cropland while the
other three products performed similarly, with producer’s
accuracies for the presence of cropland varying between
66.3% and 69.6%.
5. Discussion and Conclusions
A cropland map for Ethiopia was created using crowd-
sourced data collected via Google Earth and Geo-Wiki,
which was shown to have higher accuracy than global
land cover products in the cropland domain. However,
the user’s and producer’s accuracy for the presence of
cropland clearly indicates that there is still room for im-
provement in the crowdsourced map. In this regard there
are three main issues that deserve further discussion. The
first concerns the ability of the volunteers to identify
cropland from Google Earth images. Although some ba-
sic training materials were provided, more could be done
to control for quality, e.g. control points could be used
throughout the competition to show volunteers where
mistakes have been made. Images that were difficult to
interpret, and which were flagged by the confidence that
the volunteers placed on their interpretation, could be
discussed interactively so that others could benefit from
feedback on a variety of landscapes. These are features
that are currently not implemented but are planned for
future versions of Geo-Wiki.
A second source of error could arise from the density
of samples and the interpolation method used. Although
the samples collected through crowdsourcing covered
roughly 5% of the area of Ethiopia, there will be areas
that require a higher density of samples in order to char-
acterize them with higher accuracy. Rather than ran-
domly sampling pixels across all of Ethiopia, we could
have sampled more frequently in areas where cropland is
thought to occur, using the three global land cover maps
as a basis for driving this sampling. Moreover, as men-
tioned above, the interpolation method chosen was one of
the simplest available in order to demonstrate the feasi-
bility of this approach. Other interpolation methods and
additional data layers, e.g. a digital elevation model,
slope, rainfall and temperature, could be used to improve
the interpolation of cropland.
A third issue concerns the validation data used to cal-
culate the accuracy measures. Rather than creating a
stratified validation sample from the crowdsourced map,
existing validation data were used from a range of dif-
ferent sources, which will reflect different resolutions
and different temporal windows. However, each valida-
tion point was verified using Google Earth. We would also
argue that the change over time is a very small com-
ponent of what is a much higher uncertainty due to mis-
classification error. The validation sample consisted of
18.6% cropland and the remaining points were non-
cropland. Based on FAO statistics, the area harvested in
Ethiopia has varied between 11.2% and 13.5% over the
period 2005 to 2011 [28] so the validation dataset is only
slightly higher in terms of cropland than the FAO figures.
Although the accuracy measures indicated that the
crowdsourced cropland map performed better than the
global products, they only represent one way of judging
map quality. The difference images served to highlight
that there are large spatial differences between the
crowdsourced map and the three global land cover prod-
ucts in the cropland domain as well as between the three
global land cover maps themselves. Ultimately all of the
products must be judged by the end user, which requires
their use in different applications, ideally feeding back to
the producers where there are problems. The crowd-
sourced cropland map for Ethiopia is freely available for
downloading from the following website: http://beta- We would encourage users to ex-
periment with the commenting tools on the website to
provide us with feedback.
The bottom up approach to mapping cropland that was
demonstrated in this paper has considerable potential in
areas where cropland maps do not currently exist. Using
a motivated network of volunteers and a more targeted
sampling scheme, it would be possible to map the entire
world in this way.
6. Acknowledgements
This research was supported by the Austrian Research
Funding Agency (FFG) via the LandSpotting (No. 828332)
and Farm Support (No. 833421) projects and by the
European Community’s Seventh Framework Programme
(FP7/2007-2013) under grant agreement No. 262937
(ISAC: Information Service on Agricultural Change).
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