remote sensing
Article
Mapping Human Settlements with Higher Accuracy
and Less Volunteer Efforts by Combining Crowdsourcing
and Deep Learning
Benjamin Herfort *, Hao Li , Sascha Fendrich, Sven Lautenbach and Alexander Zipf
GIScience Chair, Institute of Geography, Heidelberg University, 69120 Heidelberg, Germany
* Correspondence: herfort@uni-heidelberg.de; Tel.: +49-6221-54-5534
Received: 26 June 2019; Accepted: 26 July 2019; Published: 31 July 2019
Abstract: Reliable techniques to generate accurate data sets of human built-up areas at national, regional,
and global scales are a key factor to monitor the implementation progress of the Sustainable Development
Goals as defined by the United Nations. However, the scarce availability of accurate and up-to-date
human settlement data remains a major challenge, e.g., for humanitarian organizations. In this paper, we
investigated the complementary value of crowdsourcing and deep learning to fill the data gaps of existing
earth observation-based (EO) products. To this end, we propose a novel workflow to combine deep
learning (DeepVGI) and crowdsourcing (MapSwipe). Our strategy for allocating classification tasks to
deep learning or crowdsourcing is based on confidence of the derived binary classification. We conducted
case studies in three different sites located in Guatemala, Laos, and Malawi to evaluate the proposed
workflow. Our study reveals that crowdsourcing and deep learning outperform existing EO-based
approaches and products such as the Global Urban Footprint. Compared to a crowdsourcing-only
approach, the combination increased the quality (measured by Matthew’s correlation coefficient) of the
generated human settlement maps by 3 to 5 percentage points. At the same time, it reduced the volunteer
efforts needed by at least 80 percentage points for all study sites. The study suggests that for the efficient
creation of human settlement maps, we should rely on human skills when needed and rely on automated
approaches when possible.
Keywords: volunteered geographic information; human settlements; deep learning; humanitarian
mapping; building detection; crowdsourcing
1. Introduction
Currently, 55% of the world’s population reside in urban areas, and especially in low-income and
lower-middle-income countries rapid urbanization is expected between now and 2050 [1]. The Sustainable
Development Goals (SDGs) [2] and the Sendai Framework for Disaster Risk Reduction (SFDRR) [3] both
highlight the relevance and increasing need for up-to-date information on the spatial distribution of
human settlements. For instance, humanitarian organizations cannot help people if they cannot find them.
Consequently, reliable techniques to generate accurate data sets of human settlements at national, regional,
and global scales are crucial in manifold domains such as disaster management, habitat and ecological
system conservation, and public health monitoring.
Earth observation (EO) using satellites already provides data for a broad range of purposes such as
disaster assessment, forestry or crop land monitoring, and land-use/land-cover classification. Recently,
Remote Sens. 2019, 11, 1799; doi:10.3390/rs11151799 www.mdpi.com/journal/remotesensing
Remote Sens. 2019, 11, 1799 2 of 21
remote sensing technologies have been successfully employed to derive information on human settlements
at regional to global scales. Accuracy and completeness of EO derived human settlement data sets have
improved a lot in the last 15 years. Current data sets, which have been made available recently, include
the Global Human Settlement Layer (GHSL) [4], the Global Urban Footprint (GUF) data set [5], and the
High-Resolution Settlement Layer (HRSL) [6]. However, these data sets still show great variations for
different regions and geographic settings [7]. Especially rural areas and non-solid building structures are
still disregarded or under-represented in these data sets.
Several researchers highlight the potential of crowdsourcing to collect information on human
settlements and to complement the data that is produced using satellite imagery [8–10]. Additionally,
humanitarian organizations start using new methods from Citizen Science and Volunteered Geographic
Information (VGI), to gather information on the spatial distribution of human settlements [11]. However,
the quality and the reliability of those methods and resulting data sets remain major concerns, which are
extensively discussed in current research [12]. Spatial varying data quality and the lack of reference data
with sufficient quality still constitute barriers in using VGI data in general and for humanitarian purposes
or in disaster management in particular [13].
The mapping of human settlements tends to be done either from an earth observation perspective
or from a citizen science position. However, a tighter integration of both approaches has the potential to
derive improved data sets that presumably outperform existing one [8].
In this article, we present a novel workflow to overcome the scarce availability of accurate and
up-to-date human settlement data sets (see Figure 1). Our proposed workflow combines two methods:
(1) object detection and classification using deep learning algorithms (DeepVGI [14]) and (2) crowdsourced
mapping of human settlements by volunteers (MapSwipe [15]). To combine both methods we propose a
task allocation strategy (3) that choses classification labels either from DeepVGI or MapSwipe. Moreover,
we investigate whether our proposed methodology helps to produce better maps faster with respect to the
following research questions.
Figure 1. Proposed workflow to combine deep learning and crowdsourcing methods: Combined labels
are obtained, by choosing labels either from DeepVGI or MapSwipe based on the confidence of the
DeepVGI labels.
Remote Sens. 2019, 11, 1799 3 of 21
• RQ1: How good are crowdsourcing (MapSwipe) and deep learning (DeepVGI) with respect to
generating human settlement maps in comparison to existing EO-based approaches?
• RQ2: Which spatial and non-spatial characteristics of misclassifications are accompanied by applying
the DeepVGI approach?
• RQ3: What is the added value of the proposed task allocation strategy with respect to performance
and effort?
The remainder of this paper is organized as follows: Section 2 provides background information on
techniques for deriving information on human settlements using either deep learning or crowdsourcing.
The study areas and data sets are described in Section 3. We present our overall methodology in Section 4
and show results in Section 5. Section 6 discusses our findings and makes suggestions for future work,
whereas Section 7 draws conclusions.
2. Background: Mapping Human Settlements Using Crowdsourcing or Deep Neural Networks
Previous research shows the strengths and shortcomings of existing global settlement data products
such as the Global Human Settlement Layer or the Global Urban Footprint data set [7,16]. In this section
we review to what degree VGI and citizen science approaches and techniques based on deep learning have
been applied to tackle the known challenges.
Citizen science projects and VGI tools are widely used to collect information on human settlements.
The OpenStreetMap (OSM) platform played a central role in generating map data during several
international disasters, e.g., after the 2010 Haiti earthquake [17] and after the 2015 Nepal earthquake [18].
Additionally, OpenStreetMap is used also in disaster preparedness and disaster risk reduction activities,
for instance, by the organizations of the Missing Maps project [11]. Regarding urban planning, Crooks et al.
(2015) [19] show how various user-generated data sets (GPS trajectories, social media data) enrich our
understanding of urban form and function from a bottom up perspective.
The increased usage of VGI has been accompanied by discussions on the quality of data sets,
which have not been produced by experts [20,21]. The research on VGI data quality reveals that
spatial heterogeneity, e.g., regarding completeness of building footprints, remains a major challenge
on different geographic scales [13]. Fan et al. (2014) [22] confirm a high completeness for building footprint
features in an urban area in Munich, Germany. For crowdsourced classification of human settlements in
Madagascar and South Sudan Herfort et al. (2017) [15] conclude that disagreement between users is not
randomly distributed in space but rather clustered, indicating that reliability of information varies spatially.
Additionally, Comber et al. (2016) [23] show for land cover mapping that the quality of VGI data sets is
influenced by differences between user groups, which are a potential source of error and uncertainty.
In addition to the above VGI-related work, several authors have investigated the potential of deep
learning technology in various satellite image processing tasks including human settlement mapping
(see [24] for a comprehensive overview). In Jean et al. (2016) [25] a convolutional neural network is used
to distinguish urban areas, non-urban area, roads, and water in optical satellite imagery for predicting
poverty in Nigeria, Tanzania, Uganda, Malawi, and Rwanda. In Li et al. (2019) [26], a pre-trained neural
network is employed to estimate large scale OSM missing built-up areas in Tanzania. Regarding land
cover mapping, several authors propose workflows based on deep neural networks with a focus on urban
areas [27,28]. Furthermore, building footprint extraction based on deep learning has been a central research
topic in recent years [29,30].
Although deep learning shows promising results with respect to object detection in images in
general, analyzing data quality of geographic approaches (e.g., using geographic data sets such as satellite
imagery and building footprints) remains a huge challenge. The transferability of deep learning models
constitutes a key challenge towards global scale data products, e.g., for human settlements. For instance,
Remote Sens. 2019, 11, 1799 4 of 21
Yuan et al. (2018) [30] highlight difficulties in extracting footprints for buildings in rural areas which
differed significantly from the footprints presented in the training set. Missing benchmark data sets tailored
to remote sensing tasks make it difficult to compare the growing number of deep learning algorithms [24].
Similar to the factors contributing to spatial heterogeneity of VGI data sets, deep learning approaches are
vulnerable to changes in input factors such as atmospheric scattering conditions, intraclass variability,
culture-dependent characteristics and a limited number of training samples [24].
In addition to studies focusing either on deep learning or on VGI approaches in isolation to generate
information on human settlements, some studies have successfully combined both approaches. A study
by Gueguen et al. (2017) [8] produces regional- and country-scale population distribution maps from
very high-resolution satellite imagery based on the detection of village boundaries by a deep neural
network and a crowdsourced validation of the results. The study reports benefits from combining
the high recall of automated methods with the high precision of human validators. By combining
data from multiple crowdsourcing projects in an active learning framework for convolutional neural
networks Chen et al. (2018) [31] address incompleteness and spatial heterogeneity of input training
samples regarding road and building mapping. Their results show a promising avenue how deep learning
can be used to improve VGI data. However, the small sample size used for validation hinders conclusions
on the transferability of their findings. Vargas-Munoz et al. (2019) [32] investigate the quality of OSM data
in study sites in Zimbabwe and Tanzania using a deep learning approach. Their approach can detect
missing building footprints and misalignment in the OSM data, but the validation data sets contain only
1000 buildings per site, which again casts doubts on the transferability of the proposed approach.
Previous work has shown how citizen science, VGI, and deep learning can contribute to improve large
scale geographic data sets on human settlements. Those methods help to produce data sets desperately
needed for monitoring urban growth, sustainable development, disaster risk reduction, and many other
applications. Nevertheless, researchers have also revealed that spatial heterogeneity is a key issue,
which needs to be addressed to understand and enhance data quality. Regarding VGI data sets, spatial
heterogeneity is expressed by regional difference in data completeness and varying data quality due to
diverging user experience. Considering deep learning approaches, spatial heterogeneity can be interpreted
as the difficulty to transfer models from one region to another and to provide training samples which
incorporate the geographic properties for all object structures or characteristics and regions.
We propose a workflow to combine the strengths of recent machine learning algorithms and
crowdsourced data production by means of a confidence-based task allocation strategy. Our work is
guided by the common hypothesis that humans rarely identify something as a building which is not a
building, but tend to miss some objects. Furthermore, deep learning approaches will miss fewer buildings
at the cost of detecting also several objects which are not buildings. By bringing together those two research
streams we aim at producing human settlement data sets which are both more complete and precise.
3. Description of the Study Areas and Data Sets
3.1. Study Areas
We investigated our combined mapping workflow at three study sites: (a) Guatemala, (b) Laos
and (c) Malawi. The study sites covered an area between 675 to 2700 square kilometers (see Table 1).
For Guatemala and Laos training regions were slightly bigger than testing regions, whereas for Malawi
the opposite was the case. Regarding the testing sites, all case studies showed an imbalanced proportion
of “no building” and “building” tiles. This imbalance was very strong towards “no building” tiles for
Guatemala and Laos (87%), and less marked for Malawi (56%).
Remote Sens. 2019, 11, 1799 5 of 21
All study regions have been mapped by OpenStreetMap users in response to requests by the
Humanitarian OpenStreetMap Team (HOT), The Netherlands Red Cross, and the Clinton Health Initiative.
Our analysis was based on the tile level. In this study a tile corresponded to the definition applied by tile
map services (TMS) at zoom level 18 [33]. Hence, each study region was made of several thousand tiles.
A tile covered around 0.02 square kilometers (0.15 × 0.15 kilometers) with slight variations depending on
geographic latitude. Table 1 summarizes the details about the training and testing areas for each study site.
Table 1. Study Sites Characteristics.
Guatemala Laos Malawi
Training Area 929.0 km2 1556.3 km2 265.7 km2
Tiles 42,833 72,360 12,408
Testing Area 745.5 km2 1136.6 km2 410.3 km2
Tiles 34,181 52,796 19,272
No Building Tiles 87% 87% 56%
The Guatemala region was made up of several different rather contrary regions. The northern and
western areas were slopes of volcanic mountains covered with dense forests, whereas urbanized areas
and agricultural land covered the valleys. Most settlements were part of a compact city with only a few
buildings lying within farmland. The Laos region was characterized by dense forested areas covering
more than 90% of the total area and by a few smaller settlements along the road network. Besides those
villages, many buildings were distributed sparsely over the entire area accompanied by smaller patches of
agricultural land. For Malawi larger cities were missing. The region was characterized by many smaller
villages and intensive agricultural land use. Only a very small fraction of the area was covered by forests.
3.2. Data Sets
This section describes the four datasets we used to evaluate the performance of our workflow.
The reference data set was derived from OpenStreetMap and depicts the presence of human settlements
in a satellite imagery tile. Building footprint geometries were directly obtained from the OpenStreetMap
database using the Overpass API by filtering for all OSM ways tagged with the key “building” for each
study site. To ensure the validity of the OSM data, we intentionally selected study areas for which
the mapping was organized through the HOT Tasking Manager tool and a validation had taken place.
This validation procedure ensured the precision of the OSM data set. More important, humanitarian
mapping experts carefully validated all tiles for which no human settlements have been mapped in OSM,
but a positive result was given either in MapSwipe or predicted by the deep neural network. Following
this additional manual validation approach ensured the completeness of the reference data set.
MapSwipe yielded results on the tile level. In MapSwipe a tile was also called a task. For each
individual tile at least three different users contributed a binary label (“building”, “no building”). These
results have been aggregated using majority voting. Furthermore, for each task the proportion of building
labels (MapSwipe score) on the total number of labels has been generated. For example, if two out of
three volunteers classified a tile as “building”, the aggregated label would be considered to be “building”
(MapSwipe score: 0.66). Further details on the MapSwipe data model can be found in [15].
The Global Urban Footprint has been derived fully automatically from TanDEM-X and TerraSAR-X
radar images with 3 m ground resolution by the German Aerospace Center (DLR) [5]. The imagery has
been collected between 2011 and 2012. In this paper, we used the binary GUF settlement mask with a
spatial resolution of 0.4”, which corresponds to a ground resolution of around 12 m at the equator.
Remote Sens. 2019, 11, 1799 6 of 21
The High-Resolution Settlement Layer maps human settlements derived from high-resolution satellite
imagery (0.5 m) by the Connectivity Lab at Facebook. The data has been produced for 18 countries using
deep neural networks [6].
4. Methodology
The workflow we propose in this paper addresses the challenge of combining deep learning and
crowdsourcing to generate high-quality human settlement maps. Section 4.1 explains the DeepVGI method
to automatically classify satellite imagery tiles into “building” and “no building”. Section 4.2 explains
the data quality evaluation procedure. In Section 4.3 we present the procedure to analyze spatial and
non-spatial characteristics of misclassifications of the DeepVGI method. Finally, Section 4.4 investigates
the proposed task allocation strategy and how the combined use of MapSwipe and DeepVGI affects
performance and volunteer efforts.
4.1. Data Preparation
We employed the DeepVGI method presented in Figure 2 for classifying satellite imagery tiles into
“no building” and “building” classes. The DeepVGI building detection model consisted of three parts:
feature extraction, object detection and binary classification.
Figure 2. DeepVGI Workflow: The DeepVGI model is trained using building footprint sample from
OpenStreetMap and satellite imagery tiles from Bing. For each tile in the testing area the model generates
label, probability and confidence score.
Based on Single Shot Detection (SSD) networks [34] the model extracted heterogeneous features from
either the base network or from extra layers. This enables SSD networks to better handle complex objects
(such as buildings) of diverse scale and shape. For doing so, SSD networks apply the concept of tiling into
default boxes so that specific feature maps learn to be responsive to particular scales of the objects [34].
The object detection generated a set of predicted building bounding boxes together with the
corresponding probability scores. The implementation of the SSD network was based on the programming
language Python 3.6 and the deep learning library Tensorflow [35]. For the initialization of the SSD
parameters, a pre-trained network based on the Microsoft COCO data set [36] has been employed, which
reported a mAP−1
(mean average precision) of 24. The maximum training epochs has been set to 60,000,
and the initial learning rate is set to 0.0004 with a momentum of 0.9. The pre-trained network is available
at the Tensorflow detection model zoo [37].
Remote Sens. 2019, 11, 1799 7 of 21
The object detection generated up to 50 bounding boxes per tile; however, the majority of those
bounding boxes did not represent buildings and were associated with very low probabilities. Since we
were interested in a binary classification of a tile t into “no building” and “building” we only selected
the highest bounding box score Pmax(t) for each tile. From the training data set derived a classification
threshold θ. Tiles with Pmax(t) below θ were most probable to belong to the “no building” class. Vice-versa
tiles with Pmax(t) above θ were most probable to belong to the “building” class“. Based on θ we generated
the label (“no building” and “building”) for each tile in the testing data set.
Finally, we derived a confidence score δt for each tile by computing the absolute difference between
the binary threshold θ and (deep learning) probability Pmax(t). This was used as a proxy for how confident
we can be in binary classification into either “building” or “no building”. Taking the absolute value eased
the visual interpretation. However, situations where the highest bounding box score was lower than
the threshold (potential false negatives) could not be distinguished from those where the bounding box
score was higher than the threshold (potential false positives) from δt alone. Hence, the analysis of the
confidence score provided insights into accuracy of the DeepVGI approach, but not towards its specificity
or sensitivity.
To train the DeepVGI model, building footprint samples from OpenStreetMap and satellite imagery
tiles from Microsoft Bing were employed. In our experiment, satellite imagery tiles were collected by
requesting a tile map service (TMS) from Microsoft Bing at zoom level 18. This corresponded to a spatial
resolution of the displayed image of roughly 0.6 m per pixel, as measured at the equator. The size of all
image tiles was 256 × 256 pixels.
4.2. Overall Performance Evaluation
We evaluated our method by investigating the quality of the produced results against the reference
data (see Section 3.2). Initially we derived the proportions of false negatives (FN), false positives (FP),
true negatives (TN), and true positives (TP). To address the imbalance of building and no building labels
in our study areas, we used the following metrics: specificity (TNR), sensitivity (TPR), and Matthews
correlation coefficient (MCC). We further derive the accuracy (ACC). The statistics were computed as
shown in Equation (1) through Equation (4). TNR, TPR and ACC are restricted between 0 and 1. MCC is
in essence a correlation coefficient between the observed and predicted binary classification. It is bound
between −1 and 1. For all statistics higher values indicate a better model fit [38].
TNR =
TN
TN + FP
(1)
TPR =
TP
TP + FN
(2)
ACC =
TP + TN
TP + FP + TN + FN
(3)
MCC =
(TP × TN) − (FP × FN)
(TP + FP) × (TP + FN) × (TN + FP) × (TN + FN)
(4)
Whereas ACC was mainly used to compare the results with works of other authors, MCC gives a
more reliable indicator on quality for imbalanced data sets [38]. TNR and TPR were used to investigate
how well the analyzed methods identify positives (“building” class) and negatives (“no building” class).
This provided insights on the strengths and weaknesses of each method. Other metrics commonly applied
for machine learning performance assessment such as F1 score or precision were not considered since they
are highly biased for imbalanced data sets [38].
Remote Sens. 2019, 11, 1799 8 of 21
Additionally, we generated a map representation of the confusion matrix for each method and study
site to spot spatial pattern in the false negatives and false positives.
4.3. Spatial and Non-Spatial Characteristics of Misclassifications
After investigating the overall performance of all methods, we conducted a detailed analysis of spatial
and non-spatial characteristics of misclassifications of the DeepVGI approach. For doing so, we generated
kernel density distribution plots of the confidence scores δt for (a) all tiles, (b) “building” tiles and (c)
“no building” tiles. The implementation of the kernel density functions was based on the programming
language Python, version 3.6 and the scipy library, version 1.1.0 [39]. The kernel bandwidth was set by
applying Scott’s rule [40]. We generated a map representation of the distribution of δt to analyze the spatial
characteristics of the confidence in the presence of buildings. This map should be interpreted together
with the maps showing the distribution of false negatives and false positives.
Furthermore, we generated conditional density plots to visualize the conditional distribution of
accuracy ACC in respect to δt and to compare the performance of the DeepVGI and MapSwipe approaches.
We tested if tasks with lower δt had a higher probability of being wrong by using a logistic regression
model for the DeepVGI and MapSwipe approaches, using δt as the predictor and Y as the response. Y
was defined as “0” for wrong classifications (e.g., DeepVGI label and reference label were not the same)
and “1” for correct classifications (e.g., DeepVGI label and reference label were the same). For the
logistic regression we report on regression coefficient, standard error, significance, and McFadden’s
pseudo-r-squared values [41]. Those were computed based on the programming language Python 3.6 and
the statsmodels library, version 0.9.0 [42].
4.4. Performance of Task Allocation Strategy
Finally, we proposed a task allocation strategy based on δt and compared the approach to a random
allocation of tiles between DeepVGI and MapSwipe. The task allocation strategy defines for which tasks
it is better to rely on results being produced by crowdsourcing and for which it is preferable to use the
DeepVGI workflow. First, tiles were sorted ascending by δt. We generated the labels based on the fusion of
both approaches by choosing a proportion α of tiles which should be labeled by the crowd (we will refer to
this as crowd proportion). For the remaining tiles we assigned the label of the DeepVGI method. Due to
this design tiles with the lowest confidence were allocated to the crowd first.
We generated 100 set of the combined labels for each study site by choosing a crowd proportion α
between [0,1] and adopting a step size of 0.01. For each α we further derived 250 random combinations of
MapSwipe and DeepVGI. For each α we investigated the performance of both task allocation strategies
in terms of accuracy ACC, Matthews correlation coefficient MCC, specificity TNR and sensitivity TPR
(described in Section 4.2). The results were visualized in a graph depicting performance in relation to the
crowd proportion α.
5. Results
5.1. Overall Performance Evaluation
This part of the results section describes the performance of different methods to create human
settlement data sets. For all study sites we analyzed data from crowdsourcing (MapSwipe), deep neural
networks (DeepVGI) and existing EO products (GUF). The HRSL data set was only available for the
Guatemala and Malawi study sites.
Remote Sens. 2019, 11, 1799 9 of 21
For all three case studies MapSwipe performed best with respect to accuracy ACC (91–96%) and
Matthews correlation coefficient MCC (80–83%) (see Table 2). The MapSwipe approach was characterized
by high specificity TNR (99% for all study sites) and intermediate sensitivity TPR (75–82%). The spatial
representation of the confusion matrices indicated that MapSwipe was able to correctly depict most
“no building” tiles. Nevertheless, for the MapSwipe approach clusters of false negatives were observed,
e.g., in the north-western part of the Guatemala study site (Figure 3) or in the south-eastern part of the
Malawi study site (Figure 5). Thus, MapSwipe users were more likely to miss buildings (false negatives),
than to map something as a building, which actually is not a building (false positives).
Regarding accuracy ACC the DeepVGI approach reached similar results to MapSwipe (91–96%);
however the differences between those approaches became slightly more distinct when considering the less
biased MCC (74–84%). The DeepVGI approach achieved lower specificity TNR compared to MapSwipe
and GUF (95–97%), but higher sensitivity TPR (81–89%). Hence, false positives were expectedly the major
concern for the DeepVGI approach. The visual interpretation of the confusion matrix maps confirmed
higher spatial concentrations of false positives. For instance, clusters of false positives were present in the
central and southern part of the Guatemala study site (Figure 3) or in the north-western part of the Malawi
study site (Figure 5).
GUF was characterized by lower ACC (58–92%) and MCC (18–60%) compared to MapSwipe or
DeepVGI. Regardless the very high specificity TNR (>99% for all study sites), the data set mapped only
parts of the buildings as described by low sensitivity TPR (6–44%). The huge number of false negatives
was the major drawback of the GUF approach. The map representation of the results for Guatemala
(Figure 3) indicates that only major settlements were captured. For the rural study sites in Laos (Figure 4)
and Malawi (Figure 5) the GUF approach generated many false negatives and thus missed most buildings.
The HRSL approach ranked below MapSwipe and DeepVGI in terms of accuracy ACC (86–90%) and
MCC (69–73%). This approach reached the best results regarding sensitivity TPR (94–96%), but achieved
only moderate specificity TNR (80–89%). Similar to the DeepVGI approach, the HRSL suffered from a
high number of false positives. This is also depicted in the maps for Guatemala (Figure 3) and Malawi
(Figure 5). It seems that false positives were located at the edges of correctly identified building tiles and
along the road network.
Table 2. Performance of different methods to generate human settlement data sets.
Guatemala Laos Malawi
TNR MapSwipe 0.99 0.99 0.99
DeepVGI 0.96 0.97 0.95
GUF 0.99 0.99 0.99
HRSL 0.89 - 0.80
TPR MapSwipe 0.74 0.79 0.82
DeepVGI 0.81 0.89 0.85
GUF 0.44 0.06 0.07
HRSL 0.96 - 0.94
ACC MapSwipe 0.96 0.97 0.91
DeepVGI 0.94 0.96 0.91
GUF 0.92 0.87 0.58
HRSL 0.90 - 0.86
MCC MapSwipe 0.80 0.85 0.83
DeepVGI 0.74 0.84 0.81
GUF 0.60 0.22 0.18
HRSL 0.69 - 0.73
Remote Sens. 2019, 11, 1799 10 of 21
Figure 3. Map representation of the confusion matrix for Guatemala: Each map shows the spatial
distribution of correct “building” (TP) and “no building” (TN) classifications for a specific method in the
testing area. Incorrect classifications are split into false positives (FP) and false negatives (FN). Each pixel
corresponds to a single task/satellite imagery tile.
5.2. Spatial and Non-Spatial Characteristics of Misclassifications
This section focuses on the analysis of the characteristics of misclassified tiles of the
DeepVGI approach.
For all study sites, the distribution density of “no building” tiles showed a clear peak, whereas the
distribution of “building” tiles was much flatter. For instance for the Guatemala study site, Figure 6 (center)
depicts that the distribution density of “no building” tiles peaked for a confidence score of around 0.25.
“Building” tiles distributed equally between confidence scores of 0.0 and 0.45. Very high confidence scores
(>0.3) are observed only for “building” tiles. At the same time, for very low confidence scores (<0.15) both
“no building” and “building” tiles were present.
Remote Sens. 2019, 11, 1799 11 of 21
Figure 4. Map representation of the confusion matrix for Laos: Each map shows the spatial distribution
of correct “building” (TP) and “no building” (TN) classifications for a specific method in the testing area.
Incorrect classifications are split into false positives (FP) and false negatives (FN). Each pixel corresponds to
a single task/satellite imagery tile.
The conditional density plots (Figure 6, blue axis) revealed the tendency that the accuracy of the
DeepVGI approach increased with higher confidence scores. For all three study sites, accuracy increased
steadily from around 50–60% to more than 95% when raising the confidence score from 0.0 to around 0.35.
There was no such clear trend for the conditional density of the accuracy for the MapSwipe approach.
MapSwipe’s accuracy ranged between 80% and 95% and indicated no dependency from the confidence
score. Additionally, the comparison of the conditional density plots from DeepVGI and MapSwipe
underlined that tiles that were relatively easy for DeepVGI (high confidence scores) were on average
not easy for MapSwipe users. Vice versa, tiles that were difficult for the DeepVGI approach (lower
confidence scores), were on average not particularly more difficult for MapSwipe users. For example for
the Guatemala study site, the DeepVGI approach provided more accurate results than MapSwipe for tiles
with a confidence score higher than 0.25. For tiles with a confidence score below 0.2 MapSwipe reached
higher accuracy.
Remote Sens. 2019, 11, 1799 12 of 21
Figure 5. Map representation of the confusion matrix for Malawi: Each map shows the spatial distribution
of correct “building” (TP) and “no building” (TN) classifications for a specific method in the testing area.
Incorrect classifications are split into false positives (FP) and false negatives (FN). Each pixel corresponds to
a single task/satellite imagery tile
The spatial distribution of the confidence score revealed highest confidence scores for major
settlements for all study sites. The most uncertain results were located in the areas with a mixed land-use,
e.g., agricultural land and settlements. Furthermore, uncertain results seemed to be clustered, e.g., for the
Laos study site (Figure 6) in most of the southern part of the study area and in a smaller area in the
northern part.
Remote Sens. 2019, 11, 1799 13 of 21
Figure 6. Spatial and Non-Spatial Distribution of the Confidence Score and Conditional Density of Accuracy
for Laos, Guatemala and Malawi. The figure provides insights on the kernel density distributions of the
confidence scores of “all” (red line), “no building” (light gray) and “building” (dark gray) tiles for the
different study regions (red axis). The same plot shows the conditional distribution of accuracy (blue axis)
for DeepVGI (solid blue line) and MapSwipe (dashed blue line). The map depicts the spatial distribution of
the confidence scores.
Remote Sens. 2019, 11, 1799 14 of 21
The indications from the conditional density plot were confirmed by the results of the logistic
regression analysis (Table 3): as depicted by the regression coefficient and the corresponding standard
error and significance with increasing confidence score, probability of correct classifications increased
significantly. However, as indicated by the low pseudo-r-squared values of (0.109–0.210) the confidence
score explained only a fraction of all misclassified tiles. The results further confirmed that the confidence
score had no explanatory power with respect to the accuracy of the MapSwipe approach (McFadden’s
Pseudo-r-squared of −0.211 to −0.130).
Table 3. Logistic Regression analysis.
Guatemala Laos Malawi
DeepVGI Coefficient 16.262 11.164 16.291
Standard Error 0.165 0.086 0.210
Significance 0.0 *** 0.0 *** 0.0 ***
McFadden’s Pseudo-r-squared 0.235 0.109 0.194
MapSwipe Coefficient 15.224 10.454 11.676
Standard Error 0.149 0.078 0.149
Significance 0.0 *** 0.0 *** 0.0 ***
McFadden’s Pseudo-r-squared −0.13 −0.154 −0.211
5.3. Combination of Crowdsourcing and Deep Learning
Figure 7 shows performance and effort in respect to crowd proportion. For all study sites allocating
10% to 20% of the tiles to MapSwipe (raising the crowd proportion from 0.0 to around 0.1–0.2) resulted
in an overall performance increase in respect to accuracy ACC and Matthew’s correlation coefficient
MCC. Reducing the volunteer efforts to one fifth (labor reduction of 80 percentage points) resulted
in a performance gain of 3–5 percentage points measured as MCC in all regions. For all study
sites, this was caused mainly due to an increase in TNR (compared to the DeepVGI-only approach).
For Guatemala and Laos, TPR remained stable, whereas for Malawi TPR increased as well (compared to
the DeepVGI-only approach).
Vice versa, allocating 10% to 30% of the tiles to DeepVGI (decreasing the crowd proportion from 1.0 to
around 0.7–0.9) also resulted in an overall performance gain. For all study sites, a gain in TPR (compared to
the MapSwipe-only approach) was observed the more tiles have been allocated to DeepVGI. For Guatemala
TNR remained stable, whereas for Laos and Malawi TNR decreased slightly at the same time.
The performance of the combined approach did not change, when allocating 20% to 70% of the sorted
tiles to MapSwipe. For crowd proportions between 0.2 and 0.7 ACC, MCC, TNR and TPR remained
mainly stable at a higher level compared to DeepVGI-only or MapSwipe-only approaches. For instance
for the Guatemala study site, MCC gained around 10 percentage points (75% to 85%) compared to the
performance of the DeepVGI-only approach by allocating 30% of the tiles to MapSwipe and 70% to
DeepVGI. At the same time this resulted in an increase in MCC of around 5 percentage points (80% to
85%) and labor reduction of 70 percentage points compared to the performance of the MapSwipe-only
approach. The MCC for a crowd proportion of 0.7 varied only slightly and reached around 84%.
When investigating the results of the random task allocation (see Figure 7) no such effects were
observed. An increase in crowd proportion resulted in an improved TNR and decreased TPR with a
uniform gradient for all study sites. Overall, ACC and MCC improved slightly with a homogeneous
slope when allocation tasks from DeepVGI to MapSwipe. However, no performance gain was observed
compared to the MapSwipe-only approach.
Remote Sens. 2019, 11, 1799 15 of 21
Figure 7. Data quality and effort in respect to crowd proportion and task allocation strategy for a combined
MapSwipe-DeepVGI methods. The upper side of the figure represents the performance of the confidence
score-based task allocation strategy, whereas on the bottom side the mean and standard deviation of the
performance of 250 random allocations between DeepVGI and MapSwipe are shown. Performance is
measured by accuracy (ACC), Matthew’s correlation coefficient (MCC), sensitivity (TPR) and specificity
(TNR). The x-axis of the plot shows the crowd proportion. A crowd proportion of 0.0 (left) refers to
DeepVGI. Consequently, the performance for a crowd proportion of 1.0 (right) refers to the performance of
MapSwipe. For a crowd proportion of 0.3, 30% of the results are obtained from MapSwipe (these tiles refer
to the DeepVGI tiles with the lowest confidence) and 70% from DeepVGI.
6. Discussion
6.1. Overall Performance Evaluation
The crowdsourcing approach by MapSwipe generated the most accurate human settlement maps
for all case study sites with Matthew’s correlation coefficient between 80% to 85%. As expressed by the
differences in specificity TNR and sensitivity TPR between the MapSwipe approach and the DeepVGI
approach, our results supported the common hypothesis that humans rarely identify something as a
building which is not a building, but tend to miss buildings. In contrast, our results indicate that deep
learning approaches tend to miss fewer buildings at the cost of also falsely detecting a range of objects
which are not buildings. In comparison to GUF, crowdsourcing and deep learning-based approaches
demonstrated an improvement in data quality for our case study sites.
Remote Sens. 2019, 11, 1799 16 of 21
The performance of MapSwipe seemed to be consistent with previous findings from Albuquerque et al.
(2016) [9], where an accuracy of 89%, a sensitivity of 73% and a precision of 89% are achieved for a very
similar crowdsourced classification task for a study site in South Kivu (Democratic Republic of the Congo).
For the case of automated village boundary detection Gueguen et al. (2017) [8] report an average precision
of around 70% and a sensitivity of around 84%. Yuan et al. (2018) [30] compare a deep learning-based
approach against GUF and GHSL for Kano city (Nigeria) and reach similar results. Their building
extraction algorithm performs with a precision of 72% and a sensitivity of 70%. Their approach slightly
improves the GUF, but significantly outperforms the Global Human Settlement Layer. Klotz et al. (2016) [7]
show that GUF and GHSL significantly increased the completeness and precision of global build-area maps
in comparison to previous low-resolution products such as MOD500 or GLOBC. Nevertheless, the authors
also point out quantifiable weaknesses in rural areas, which could be confirmed by our study results as
well. Whereas the GUF was of moderate quality for the sub-urban Guatemala study site, its weakness for
the rural study sites in Laos and Malawi was immense.
Nevertheless, our results also suffered from limitations in our data sets and methods applied. Due
to the imbalance of “no building” and “building” tiles, the accuracy ACC reported was biased towards
identifying “no building” tiles correctly. This imbalance was strong for Guatemala and Laos, but less
pronounced for Malawi. Whereas TNR and TPR show no bias, Matthew’s correlation coefficient is biased
as well (but not as strong as accuracy) [38]. This reduces the comparability of our results with the findings
from other studies with less imbalanced data.
In this study, we did not investigate the effects of the imbalance on the training procedure of the
DeepVGI approach. Furthermore, we decided to use a very specific network architecture and pre-trained
model (SSD based on COCO data set, see Section 4.1). Our two-step approach (object detection first, then
binary classification) also introduced further uncertainties. Whereas our results seemed to be consistent
with the findings from other studies, further research is necessary to fully understand the impact of the
data preparation on performance. For instance, Tiecke et al. (2017) [6] present a computer vision method
to create population maps from satellite imagery with a very high resolution and provide further insights
on potential systematic errors. The authors highlight the problem related to repetitive errors, such as the
misinterpretation of large rocks, boats, or mountain ridges as buildings. Analyzing the potential sources of
systematic errors would be of great benefit for our study to understand spatial clusters of false positives.
New advances in machine learning research might produce architectures and training data sets which suit
better to the specific use case of mapping human settlements. To reduce uncertainties in our approach,
future studies should compare different architectures and training data set characteristics also regarding
imbalanced classes.
Uncertainties were also present for the results of the MapSwipe approach. Using majority aggregation
to generate binary labels from the individual user classifications favored higher specificity TNR, whereas
choosing another method might have promoted higher TPR. The drawbacks of majority agreement are
well described by Salk et al. (2016) [43]. The confusion matrix maps showed that wrong classifications were
not randomly distributed, but revealed spatial pattern. For MapSwipe this has already been confirmed
in Herfort et al. (2017) [15]. For our study, this implies that individual user behavior, geographical
characteristics of the surrounding of building features might be major causes of wrong classifications
limiting the transferability of this approach. A more detailed analysis is necessary to understand the
factors which drive the quality of crowdsourcing and its implications for human settlement maps.
Satellite imagery quality is another major concern for both crowdsourcing and deep learning
approaches. Better satellite imagery (e.g., in terms of resolution), might strongly influence the performance
of MapSwipe and DeepVGI. Our current study was limited to satellite imagery tiles at zoom level 18.
For the regions analyzed in this study, satellite imagery tiles with a higher image resolution at zoom level
19 were not available from Bing Maps. Nevertheless, new earth observation satellites such as WorldView3
Remote Sens. 2019, 11, 1799 17 of 21
would potentially provide sufficient imagery data for this zoom level. The scarce availability of up-to-date
satellite imagery, emphasizes another drawback of MapSwipe and DeepVGI: the quality of the human
settlement maps is closely tied to the structures visible in the satellite images. In situations where
settlement patterns change rapidly, e.g., due to forced displacement, on the ground data is irreplaceable
unless up-to-date satellite data becomes available.
6.2. Spatial and Non-Spatial Characteristics of Misclassifications
Our analysis provided insights into the spatial and non-spatial characteristics of misclassified tiles of
the DeepVGI method. For all study sites the conditional density plots and logistic regression analyses
revealed a significant correlation between confidence score and accuracy. Additionally, the results showed
no such correlation for the MapSwipe data set. This indicates that DeepVGI and MapSwipe tended to
detect different tiles with different characteristics at varying accuracy. We interpreted this as potential
complementary value of both approaches.
However, the limited explanatory power of accuracy for imbalanced data sets needs to be considered
for our study (especially for Guatemala and Laos). Hence, the increase in accuracy mainly depicted the
correlation between specificity TNR and confidence score. The design of the confidence score (using
absolute values, see Section 4.3) hampered the differentiation of false positives and false negatives.
Whereas this simplification turned out to be beneficial for the logistic regression analysis, using a more
sophisticated method, e.g., relying on a quadratic function, would have reduced the bias introduced due
to class imbalance. Furthermore, higher confidence scores might be also related to the number of detected
buildings per tile. In our approach, the confidence score was based only on the most probable building
detection and did not consider multiple detections per tile. This approach increased uncertainties for tiles,
for which only a few buildings were located, e.g., in rural areas.
The results of the logistic regression analysis highlighted that confidence score contributed to the
probability of tiles being misclassified, but only to a minor fraction. For the case of land cover mapping
with a focus on urban areas Kampffmeyer et al. (2016) [27] provide similar findings and show that pixels
with low uncertainty are more likely to be classified correctly. As in our study, areas of class boundaries
were a cause of wrong classifications. However, our study showed as well that most misclassified tiles
had a different cause not captured by our design. Our methods fell short especially in differentiating the
confidence of “no building” classifications, which however constituted most tasks in our study areas.
Further research is needed to expand our understanding of the systematic errors underlying our
approach. For example, we would investigate to what degree deep learning-based methods are able to
map various building types, e.g., in relation to shape and size or to characteristics related to ethnic or
social groups.
6.3. Combination of Crowdsourcing and Deep Learning
Combining the MapSwipe and DeepVGI methods using the confidence score-based task allocation
strategy increased performance by around 3 - 5 percentage points measured by MCC (compared to the
MapSwipe-only approach). At the same time, the approach reduced the volunteer efforts to one fifth (labor
reduction of 80 percentage points). Our results suggest that the task allocation strategy helped to exploit
the complementary value of a sensitive method (DeepVGI) and a specific method (MapSwipe) and would
improve the existing crowdsourcing approach MapSwipe uses.
For a similar set up but limited geographic scope Chen et al. (2018) [31] show that a combination
of results from machine learning and crowdsourcing can result in a labor reduction of 85 percentage
points and achieves a similar accuracy. Gueguen et al. (2017) [8] report similar findings regarding
Remote Sens. 2019, 11, 1799 18 of 21
semi-supervised village boundary mapping and are able to improve the precision of automated data
extraction by introducing a crowdsourced validation step.
The combination of crowdsourcing and deep learning showed promising results for our selected case
studies, but limitations of the presented workflow must be considered. First, the individual performance
of both methods might have a great impact on the performance of the combined approach. Our approach
was able to improve results because of the complementary characteristics MapSwipe (high TNR) and
DeepVGI (high TPR) hold. Due to the uncertainties of crowdsourcing and deep learning (discussed in
Section 6.1) these differences between the two methods might be less pronounced or even reversed in
other regions.
Considering the needs of humanitarian organizations (e.g., getting information on human settlements
for which no other data sets exist) our approach could be used in real applications. However, the current
workflow was not able to provide a clear estimation which crowd proportion would generate optimal
results beforehand. Project managers organizing humanitarian mapping campaigns would still need to
adjust the proportion of tasks mapped by the crowd manually, e.g., in respect to the given time frame and
complexity of the mapping task.
We tested the workflow for three rather diverse study sites, nevertheless a more detailed investigation
of the influence of geographic characteristics is necessary. Quantifying the differences between the study
sites (e.g., in respect to land cover) would be a first step towards contextualizing the results. Together
with an analysis of the quality of the satellite imagery and quality of the crowdsourced classifications this
would help to understand for which study site characteristics a combination might result in better data.
7. Conclusions
Human settlement maps produced by crowdsourcing (MapSwipe) or deep learning (DeepVGI)
showed large overlaps and for most areas both methods generated results with a similar accuracy.
In general, both methods outperformed existing EO-based products such as the Global Urban Footprint in
terms of MCC and TPR. The proposed confidence score indicator helped to explain misclassified tiles of
the DeepVGI method and revealed the complementary value of DeepVGI and MapSwipe. Combining
crowdsourcing and deep learning by applying the proposed task allocation strategy facilitated the
complementary values of both methods and provided a promising extension to the existing crowdsourcing
approach MapSwipe incorporates.
Further research needs to validate these findings also for other study regions and various settlement
types and shapes and contextual features such as vegetation and land use. The large amount of finished
MapSwipe projects provides an obvious starting point for such an extended geographical analysis.
Our study focused on the binary classification of satellite imagery tiles to map human settlements.
Due to the structure of the MapSwipe results, the analysis was limited to the tile level. Future research
should overcome this drawback and investigate human settlement classifications at a more fine-grained
resolution and/or move on to investigating automatically generated building footprint geometries. Initial
research in this direction has been conducted by Vargas-Munoz et al. (2019) [32]; however national and
regional investigations are necessary.
Reaching the targets of the sustainable development, planning disaster responses more efficiently and
reducing the vulnerability of people at risk before disasters occur will remain challenges for the upcoming
decades. The proposed combined use of satellite data, deep learning technology and citizen-based
observations showed great potential to contribute to those efforts and future applications should consider
the lessons learned from this research. However, to take fully advantage of the new opportunities there is a
need to further understand the technical and non-technical challenges that come with them. The presented
approach might help to identify data quality issues during or immediately after an object has been mapped
Remote Sens. 2019, 11, 1799 19 of 21
to provide real-time or near real-time feedback for mappers. However, understanding and communicating
the quality of automatically generated results and ensuring that tools and data are open and accessible are
the very basis for this.
Integrating machine learning techniques into existing crowdsourcing applications will also create an
increased need for technical knowledge for project managers and data users. From the citizen science and
VGI projects perspective, introducing these new techniques might also lead to an increased demand for
experienced validators, which are already few in number, presently. Whereas we envision to use these
new tools to improve overall data quality and availability, they also constitute a new potential source of
bias introduced into data sets such as OpenStreetMap. This bias might also be caused by class imbalances,
which were present also in our study.
Taking all this into account, our results endorsed that for the creation of human settlement maps, we
should rely on automated approaches (e.g., machine learning) when possible, but rely on human skills
(e.g., citizens science and crowdsourcing) when needed.
Author Contributions: Conceptualization, B.H. and H.L.; Methodology, B.H., H.L. and S.F.; Validation, B.H., H.L. and
S.F.; Formal Analysis, B.H.; Writing—Original Draft Preparation, B.H. and H.L.; Writing—Review and Editing, B.H.,
S.F. and S.L.; Visualization, B.H.; Supervision, A.Z.; Project Administration, S.F. and A.Z.
Funding: The authors acknowledge financial support by Deutsche Forschungsgemeinschaft within the funding
programme Open Access Publishing, by the Baden-Württemberg Ministry of Science, Research and the Arts and by
Ruprecht-Karls-Universität Heidelberg. This research was partly funded by the Klaus Tschira Stiftung, Heidelberg.
Conflicts of Interest: The authors declare no conflict of interest.
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